diff --git a/docs/developer_guides/debugging_tools/benchmarking.md b/docs/developer_guides/debugging_tools/benchmarking.md index b38c09277..5e213fb1b 100644 --- a/docs/developer_guides/debugging_tools/benchmarking.md +++ b/docs/developer_guides/debugging_tools/benchmarking.md @@ -9,7 +9,7 @@ To launch training jobs automatically on each of these items, you can call: ```bash -./nerfstudio/scripts/benchmarking/launch_train_blender.sh -m {METHOD_NAME} [-s] [-v {VIS}] [{GPU_LIST}] +./scripts/benchmarking/launch_train_blender.sh -m {METHOD_NAME} [-s] [-v {VIS}] [{GPU_LIST}] ``` Simply replace the arguments in brackets with the correct arguments. @@ -18,31 +18,31 @@ Simply replace the arguments in brackets with the correct arguments. - `-s`: Launch a single job per GPU. - `-v {VIS}`: Use another visualization than wandb, which is the default. Only other option is tensorboard. - `{GPU_LIST}`: (optional) Specify the list of gpus you want to use on your machine space separated. for instance, if you want to use GPU's 0-3, you will need to pass in `0 1 2 3`. If left empty, the script will automatically find available GPU's and distribute training jobs on the available GPUs. - + :::{admonition} Tip :class: info -To view all the arguments and annotations, you can run `./nerfstudio/scripts/benchmarking/launch_train_blender.sh --help` -::: +To view all the arguments and annotations, you can run `./scripts/benchmarking/launch_train_blender.sh --help` + ::: + A full example would be: - Specifying gpus - - ```bash - ./nerfstudio/scripts/benchmarking/launch_train_blender.sh -m nerfacto 0 1 2 3 - ``` + ```bash + ./scripts/benchmarking/launch_train_blender.sh -m nerfacto 0 1 2 3 + ``` - Automatically find available gpus - ```bash - ./nerfstudio/scripts/benchmarking/launch_train_blender.sh -m nerfacto - ``` + ```bash + ./scripts/benchmarking/launch_train_blender.sh -m nerfacto + ``` The script will automatically launch training on all of the items and save the checkpoints in an output directory with the experiment name and current timestamp. ## Evaluating trained Blender models -Once you have launched training, and training converges, you can test your method with `nerfstudio/scripts/benchmarking/launch_eval_blender.sh`. +Once you have launched training, and training converges, you can test your method with `scripts/benchmarking/launch_eval_blender.sh`. Say we ran a benchmark on 08-10-2022 for `instant-ngp`. By default, the train script will save the benchmarks in the following format: @@ -65,7 +65,7 @@ If we wanted to run the benchmark on all the blender data for the above example, ```bash -./nerfstudio/scripts/benchmarking/launch_eval_blender.sh -m instant-ngp -o outputs/ -t 2022-08-10_172517 [{GPU_LIST}] +./scripts/benchmarking/launch_eval_blender.sh -m instant-ngp -o outputs/ -t 2022-08-10_172517 [{GPU_LIST}] ``` The flags used in the benchmarking script are defined as follows: @@ -90,4 +90,4 @@ outputs :class: warning Since we are running multiple backgrounded processes concurrently with this script, please note the terminal logs may be messy. -::: + ::: diff --git a/docs/developer_guides/debugging_tools/profiling.md b/docs/developer_guides/debugging_tools/profiling.md index aabada5f4..ed8796cec 100644 --- a/docs/developer_guides/debugging_tools/profiling.md +++ b/docs/developer_guides/debugging_tools/profiling.md @@ -23,24 +23,7 @@ class RayGenerator(nn.Module): ... ``` -Alternatively, you can also time parts of the code: -```python -... - -def forward(self, ray_indices: TensorType["num_rays", 3]) -> RayBundle: - # implementation here - with profiler.time_function("code1"): - # some code here - ... - - with profiler.time_function("code2"): - # some code here - ... - ... -``` - - -At termination of training or end of the training run, the profiler will print out the average execution time for all of the functions or code blocks that have the profiler tag. +At termination of training or end of the training run, the profiler will print out the average execution time for all of the functions that have the profiler tag. :::{admonition} Tip :class: info @@ -49,14 +32,6 @@ Use this profiler if there are *specific/individual functions* you want to measu ::: -#### Profiling with PyTorch profiler - -If you want to profile the training or evaluation code and track the memory and CUDA kernel launches, consider using [PyTorch profiler](https://pytorch.org/tutorials/recipes/recipes/profiler_recipe.html). -It will run the profiler for some selected step numbers, once with `CUDA_LAUNCH_BLOCKING=1`, once with `CUDA_LAUNCH_BLOCKING=0`. -The PyTorch profiler can be enabled with `--logging.profiler=pytorch` flag. -The outputs of the profiler are trace files stored in `{PATH_TO_MODEL_OUTPUT}/profiler_traces`, and can be loaded in Google Chrome by typing `chrome://tracing`. - - #### Profiling with PySpy If you want to profile the entire codebase, consider using [PySpy](https://github.com/benfred/py-spy). diff --git a/docs/developer_guides/new_methods.md b/docs/developer_guides/new_methods.md index baafad440..661629d15 100644 --- a/docs/developer_guides/new_methods.md +++ b/docs/developer_guides/new_methods.md @@ -10,7 +10,6 @@ We recommend the following file structure: ``` ├── my_method -│ ├── __init__.py │ ├── my_config.py │ ├── custom_pipeline.py [optional] │ ├── custom_model.py [optional] @@ -57,9 +56,6 @@ dependencies = [ "nerfstudio" # you may want to consider pinning the version, ie "nerfstudio==0.1.19" ] -[tool.setuptools.packages.find] -include = ["my_method*"] - [project.entry-points.'nerfstudio.method_configs'] my-method = 'my_method.my_config:MyMethod' ``` @@ -70,12 +66,6 @@ finally run the following to register the method, pip install -e . ``` -When developing a new method you don't always want to install your code as a package. -Instead, you may use the `NERFSTUDIO_METHOD_CONFIGS` environment variable to temporarily register your custom method. -``` -export NERFSTUDIO_METHOD_CONFIGS="my-method=my_method.my_config:MyMethod" -``` - ## Running custom method After registering your method you should be able to run the method with, diff --git a/docs/developer_guides/viewer/custom_gui.md b/docs/developer_guides/viewer/custom_gui.md index e11381d02..09275dac1 100644 --- a/docs/developer_guides/viewer/custom_gui.md +++ b/docs/developer_guides/viewer/custom_gui.md @@ -33,15 +33,16 @@ current_value = self.custom_value.value **Callbacks** You can register a callback that will be called whenever a new value for your GUI element is available. For example, one can use a callback to update config parameters when elements are changed: ```python -def on_change_callback(handle: ViewerCheckbox) -> None: - self.config.example_parameter = handle.value +def on_change_callback(handle: GuiHandle): + self.config.example_parameter = handle.get_value() self.custom_checkbox = ViewerCheckbox( name="Checkbox", - default_value=False, - cb_hook=on_change_callback, + default_value = False, + cb_hook = on_change_callback ) ``` +The GuiHandle value is a viser object which you can use to get the new value. **Thread safety** Note that `ViewerElement` values can change asynchronously to model execution. So, it's best practice to store the value of a viewer element once at the beginning @@ -62,13 +63,13 @@ class MyModel(Model): You can write to a viewer element in Python, which provides a convenient way to track values in your code without the need for wandb/tensorboard or relying on `print` statements. ```python -self.custom_value.value = x +self.custom_value = x ``` :::{admonition} Warning :class: warning -Updating module state while training can have unexpected side effects. It is up to the user to ensure that GUI actions are safe. Conditioning on `self.training` can help determine whether effects are applied during forward passes for training or rendering. +Note that updating module state while training can have unexpected side effects. It is up to the user to ensure that GUI actions are safe. In most cases, we recommend wrapping GUI related logic in `if self.training:` to ensure that the effects are only applied during inference. ::: ## Example Elements diff --git a/docs/extensions/blender_addon.md b/docs/extensions/blender_addon.md index 8e2fa4bd2..842d9bb38 100644 --- a/docs/extensions/blender_addon.md +++ b/docs/extensions/blender_addon.md @@ -16,7 +16,7 @@ This Blender add-on allows for compositing with a Nerfstudio render as a backgro 1. The add-on requires Blender 3.0 or newer, install Blender [here](https://www.blender.org/). -2. Download Blender Add-on Script +2. Download Blender Add-on Script 3. Install and enable the add-on in Blender in `Edit → Preferences → Add-Ons`. The add-on will be visible in the Render Properties tab on the right panel.
@@ -136,34 +136,34 @@ You can composite NeRF objects into a scene with a NeRF background by rendering 1. Import the background NeRF scene as a point cloud (or mesh, but point cloud is preferred for large scenes). 2. Export a cropped NeRF mesh of the NeRF object(s) + - Open the NeRF object from the Nerfstudio viewer and select "Crop Viewport" and accordingly adjust the Scale and Center values of the bounding box to crop the NeRF scene to around the bounds of the object of interest. - - Open the NeRF object from the Nerfstudio viewer and select "Crop Viewport" and accordingly adjust the Scale and Center values of the bounding box to crop the NeRF scene to around the bounds of the object of interest. +
+ image +
+ + - Copy over the scale and center values into the Export panel and export the NeRF object as a mesh (point cloud will also work but shadows can be rendered if the object is a mesh) -
- image -
+ - Keep note of the scale and center values for the crop. This can be done by creating a new JSON camera path in the editor which will add a crop section towards the end of the file. - - Copy over the scale and center values into the Export panel and export the NeRF object as a mesh (point cloud will also work but shadows can be rendered if the object is a mesh) - - - Keep note of the scale and center values for the crop. This can be done by creating a new JSON camera path in the editor which will add a crop section towards the end of the file. - -
- image -
+
+ image +
+ 3. Import the NeRF object representation. Rescale and position the scene and NeRF object and background environment. You can also animate either of them. 4. (Optional) To add shadows of the NeRF object(s) + - Add a plane representing the ground of the environment. In object properties, select "Shadow Catcher" under Visibility. You may have to switch to the cycles renderer to see the shadow catcher option. - - Add a plane representing the ground of the environment. In object properties, select "Shadow Catcher" under Visibility. You may have to switch to the cycles renderer to see the shadow catcher option. + - In the object properties of the NeRF object, go to the Ray Visibility section and deselect the "Camera" option. This will hide the mesh in the Blender render, but keep its shadow. + - - In the object properties of the NeRF object, go to the Ray Visibility section and deselect the "Camera" option. This will hide the mesh in the Blender render, but keep its shadow. - -
- image -
- - - Render the Blender animation with only the shadow of the object on the shadow catcher visible. Render with a transparent background by selecting "Transparent" under the Film Settings in the Render Properties. +
+ image +
+ + - Render the Blender animation with only the shadow of the object on the shadow catcher visible. Render with a transparent background by selecting "Transparent" under the Film Settings in the Render Properties. 5. Go to the Nerfstudio Add-on in Render Properties and expand the "Nerfstudio Path Generator" tab in the panel. Use the object selector to select the NeRF environment representation. Then, select the file path for the output JSON camera path file. Click "Generate JSON file". The output JSON file is named `camera_path_blender.json`. It is recommended to rename this JSON file to keep track of the multiple camera paths that will be used to construct the full render. @@ -172,32 +172,29 @@ You can composite NeRF objects into a scene with a NeRF background by rendering 7. Render the NeRF of the background environment using the generated camera path for the environment using the Nerfstudio in the command line or terminal. 8. Render the NeRF object + - Open the recently generated blender Nerfstudio JSON camera path and add the crop parameters to the camera path. This section will be placed towards the end of the Blender Nerfstudio camera path after the `is_cycle` field. This can be done by copying over the crop section from the JSON camera path with the scene crop which was exported earlier. Alternatively, you can enter the crop values manually in this format. +
+ image +
- - Open the recently generated blender Nerfstudio JSON camera path and add the crop parameters to the camera path. This section will be placed towards the end of the Blender Nerfstudio camera path after the `is_cycle` field. This can be done by copying over the crop section from the JSON camera path with the scene crop which was exported earlier. Alternatively, you can enter the crop values manually in this format. -
- image -
+ - Render the NeRF object using Nerfstudio and the edited camera path in the command line or terminal. This will be the RGB render. - - Render the NeRF object using Nerfstudio and the edited camera path in the command line or terminal. This will be the RGB render. + - Next, render the accumulation render as an alpha mask of the NeRF object by adding the command line argument `--rendered-output-names accumulation` to the render command. - - Next, render the accumulation render as an alpha mask of the NeRF object by adding the command line argument `--rendered-output-names accumulation` to the render command. - -
- image -
+
+ image +
9. Convert each of the Nerfstudio render videos into an image sequence of frames as PNG or JPG files. This will ensure that the frames will be aligned when compositing. You can convert the video mp4 to an image sequence by creating a Blender Video Editing file and rendering the mp4 as JPGs or PNGs. -10. Composite the NeRF renders in a video editing software such as Adobe Premiere Pro. - +10. Composite the NeRF renders in a video editing software such as Adobe Premiere Pro. - Place the render of the Nerfstudio background environment at the lowest layer, then place the shadow render of the NeRF object if created. - - Place the RGB NeRF render of the NeRF object over the environment (and shadow if present) layers and then place the accumulation NeRF object render over the RGB NeRF object render. + - Place the RGB NeRF render of the NeRF object over the environment (and shadow if present) layers and then place the accumulation NeRF object render over the RGB NeRF object render. - Apply a filter to use the accumulation render as an alpha mask. In Premiere Pro, apply the effect "Track Matte Key" to the RGB render and select the "Matte" as the video track of the accumulation render and under "Composite Using" select "Matte Luma". ### Additional Details - - The RGB and accumulation renders will need to be rendered for each NeRF cropped object in the scene, but not for the NeRF environment if it is not cropped. - If you will composite a shadow layer, the quality of the exported mesh of the NeRF object should have enough fidelity to cast a shadow, but the texture doesn't need to be clear. - If motion tracking or compositing over real camera footage, you can add planes or cubes to represent walls or doorways as shadow catcher or holdout objects. This will composite the shadow layer over the NeRF environment and help create alpha masks. @@ -205,7 +202,6 @@ You can composite NeRF objects into a scene with a NeRF background by rendering - A walkthrough of this section is included in the tutorial video. ### Examples -

image image diff --git a/docs/nerfology/methods/index.md b/docs/nerfology/methods/index.md index 441a6d705..68912bb7b 100644 --- a/docs/nerfology/methods/index.md +++ b/docs/nerfology/methods/index.md @@ -31,9 +31,7 @@ The following methods are supported in nerfstudio: Nerfacto Instant-NGP Instruct-NeRF2NeRF - LERF Semantic NeRF-W - Tetra-NeRF ``` (own_method_docs)= diff --git a/docs/nerfology/model_components/visualize_samplers.ipynb b/docs/nerfology/model_components/visualize_samplers.ipynb index a91c754e2..a23500d58 100644 --- a/docs/nerfology/model_components/visualize_samplers.ipynb +++ b/docs/nerfology/model_components/visualize_samplers.ipynb @@ -1,7441 +1,7441 @@ { - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Ray Samplers\n", - "\n", - "## Overview\n", - "\n", - "Once we have a set of cameras, we want to cast camera rays associated with each pixel. \n", - "\n", - "Along these ray we will sample the _field_ and aggregate the samples to predict the pixels value (ie. color). The parameterization of the samples are described [here](./visualize_samples.ipynb) however we must decide where to place these samples along a ray. For this task we will use a [`Sampler`](../../reference/api/model_components/ray_sampler.rst).\n", - "\n", - "In the ideal world we would compute many dense samples along a ray. Unfortunately, each additional sample adds a computation cost to the system as it needs to be processed by the _field_ which is often a neural network.\n", - "\n", - "As a result it is common for NeRF methods to use on the order of 100 samples. Therefore, we want to optimize where those samples are placed in the scene.\n", - "\n", - "For example, if the scene can be bounded by a box and the objects are all similar scales, uniform sampling along the ray may be a good option. On the other hand if the scene is unbounded (potential extending as far as the eye can see) uniform sampling does not make sense as the samples would be very sparse for close objects. In this case a different sampling like _Uniform in Disparity_ may perform better.\n", - "\n", - "```{image} imgs/samplers_type-light.png\n", - ":align: center\n", - ":class: only-light\n", - "```\n", - "\n", - "```{image} imgs/samplers_type-dark.png\n", - ":align: center\n", - ":class: only-dark\n", - "```\n", - "\n", - "### Stratified Sampling\n", - "\n", - "Most samplers has the option to _stratify_ the samplers. When stratified, each sample is randomly perturbed. \n", - "\n", - "The magnitude of the pertubation is such that the sample ordering remains consistent and the overall distribution statistics are not changed. Using stratified samples during training generally improves the reconstructions as it help prevent overfitting.\n", - "\n", - "During inference stratified sampling should be disabled (nerfstudio samplers will do this) as it can cause noisy artifacts when the camera moves.\n", - "\n", - "```{image} imgs/samplers_stratified-light.png\n", - ":align: center\n", - ":class: only-light\n", - "```\n", - "\n", - "```{image} imgs/samplers_stratified-dark.png\n", - ":align: center\n", - ":class: only-dark\n", - "```\n", - "\n", - "### Hierarchical Sampling\n", - "\n", - "It is important to sample the scene where it has content otherwise the reconstruction quality will be reduced. \n", - "\n", - "One trick that is often employed in NeRF methods is to do multiple round of sampling. The first round can use a predefined sampler (ie. Uniform) to generate an image. Once the space is sampled, we have an idea which samples contributed to the final color. \n", - "\n", - "We can use this information to sample more around those regions using a `PDFSampler`. The PDF sampler is described more below." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Spaced Samplers\n", - "\n", - "These are the most basic samplers that spaces samples based on a predefined function. These samplers have all have a starting and ending distance (also known as a near/far plane). The plots below are histograms of points sampled from some predefined samplers. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "3e6b9d9b42cb4dc89f40a570007d00ec", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Output()" - ] - }, - "metadata": {}, - "output_type": "display_data" + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ray Samplers\n", + "\n", + "## Overview\n", + "\n", + "Once we have a set of cameras, we want to cast camera rays associated with each pixel. \n", + "\n", + "Along these ray we will sample the _field_ and aggregate the samples to predict the pixels value (ie. color). The parameterization of the samples are described [here](./visualize_samples.ipynb) however we must decide where to place these samples along a ray. For this task we will use a [`Sampler`](../../reference/api/model_components/ray_sampler.rst).\n", + "\n", + "In the ideal world we would compute many dense samples along a ray. Unfortunatly each additional sample adds a computation cost to the system as it needs to be processed by the _field_ which is often a neural network. \n", + "\n", + "As a result it is common for NeRF methods to use on the order of 100 samples. Therefore we want to optimize where those samples are placed in the scene.\n", + "\n", + "For example, if the sene can be bounded by a box and the objects are all similar scales, uniform sampling along the ray may be a good option. On the other hand if the scene is unbounded (potential extending as far as the the eye can see) uniform sampling does not make sense as the samples would be very sparse for close objects. In this case a different sampling like _Uniform in Disparity_ may perform better.\n", + "\n", + "```{image} imgs/samplers_type-light.png\n", + ":align: center\n", + ":class: only-light\n", + "```\n", + "\n", + "```{image} imgs/samplers_type-dark.png\n", + ":align: center\n", + ":class: only-dark\n", + "```\n", + "\n", + "### Stratified Sampling\n", + "\n", + "Most samplers has the option to _stratify_ the samplers. When stratified, each sample is randomly perturbed. \n", + "\n", + "The magnitude of the pertubation is such that the sample ordering remains consistent and the overal distribution statistics are not changed. Using stratified samples during training generally improves the reconstructions as it help prevent overfitting. \n", + "\n", + "During inference stratified sampling should be disabled (nerfstudio samplers will do this) as it can cause noisy artifacts when the camera moves.\n", + "\n", + "```{image} imgs/samplers_stratified-light.png\n", + ":align: center\n", + ":class: only-light\n", + "```\n", + "\n", + "```{image} imgs/samplers_stratified-dark.png\n", + ":align: center\n", + ":class: only-dark\n", + "```\n", + "\n", + "### Hierarchical Sampling\n", + "\n", + "It is important to sample the scene where it has content otherwise the reconstruction quality will be reduced. \n", + "\n", + "One trick that is often employed in NeRF methods is to do multiple round of sampling. The first round can use a predefined sampler (ie. Uniform) to generate an image. Once the space is sampled, we have an idea which samples contributed to the final color. \n", + "\n", + "We can use this information to sample more around those regions using a `PDFSampler`. The PDF sampler is described more below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Spaced Samplers\n", + "\n", + "These are the most basic samplers that spaces samples based on a predefined function. These samplers have all have a starting and ending distance (also known as a near/far plane). The plots below are histograms of points sampled from some predefined samplers. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3e6b9d9b42cb4dc89f40a570007d00ec", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

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+                                    "anchor": "y2",
+                                    "domain": [
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+                                    "matches": "x4",
+                                    "showticklabels": false
+                                },
+                                "xaxis3": {
+                                    "anchor": "y3",
+                                    "domain": [
+                                        0,
+                                        0.45
+                                    ],
+                                    "title": {
+                                        "text": "Distance"
+                                    }
+                                },
+                                "xaxis4": {
+                                    "anchor": "y4",
+                                    "domain": [
+                                        0.55,
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+                                    "title": {
+                                        "text": "Distance"
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+                                },
+                                "yaxis": {
+                                    "anchor": "x",
+                                    "domain": [
+                                        0.55,
+                                        1
+                                    ],
+                                    "range": [
+                                        0,
+                                        80
+                                    ],
+                                    "title": {
+                                        "text": "# Samples"
+                                    }
+                                },
+                                "yaxis2": {
+                                    "anchor": "x2",
+                                    "domain": [
+                                        0.55,
+                                        1
+                                    ],
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+                                    "domain": [
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+                                    "range": [
+                                        0,
+                                        80
+                                    ],
+                                    "title": {
+                                        "text": "# Samples"
+                                    }
+                                },
+                                "yaxis4": {
+                                    "anchor": "x4",
+                                    "domain": [
+                                        0,
+                                        0.45
+                                    ],
+                                    "matches": "y3",
+                                    "range": [
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+                            }
+                        },
+                        "text/html": [
+                            "
                            
                    
"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "# COLLAPSED\n",
+                "import torch\n",
+                "from nerfstudio.cameras.rays import RayBundle\n",
+                "from nerfstudio.model_components import ray_sampler\n",
+                "import plotly.graph_objects as go\n",
+                "from plotly.subplots import make_subplots\n",
+                "\n",
+                "\n",
+                "num_samples = 1000\n",
+                "near = 2\n",
+                "far = 5\n",
+                "train_stratified = False\n",
+                "\n",
+                "samplers = [\n",
+                "    ray_sampler.UniformSampler,\n",
+                "    ray_sampler.LinearDisparitySampler,\n",
+                "    ray_sampler.SqrtSampler,\n",
+                "    ray_sampler.LogSampler,\n",
+                "]\n",
+                "\n",
+                "fig = make_subplots(\n",
+                "    rows=2,\n",
+                "    cols=2,\n",
+                "    subplot_titles=(\"Uniform\", \"Linear in Disparity\", \"Square Root\", \"Log Sampler\"),\n",
+                "    shared_xaxes=True,\n",
+                "    shared_yaxes=True,\n",
+                "    vertical_spacing=0.1,\n",
+                ")\n",
+                "\n",
+                "for i, Sampler in enumerate(samplers):\n",
+                "    sampler = Sampler(num_samples=num_samples, train_stratified=train_stratified)\n",
+                "\n",
+                "    ray_bundle = RayBundle(\n",
+                "        origins=torch.ones([1, 3]),\n",
+                "        directions=torch.ones([1, 3]),\n",
+                "        pixel_area=torch.ones([1, 1]),\n",
+                "        nears=torch.ones([1, 1]) * near,\n",
+                "        fars=torch.ones([1, 1]) * far,\n",
+                "    )\n",
+                "\n",
+                "    samples = sampler.generate_ray_samples(ray_bundle)\n",
+                "\n",
+                "    trace = go.Histogram(x=samples.frustums.starts[0, :, 0], nbinsx=50)\n",
+                "    fig.append_trace(trace, i // 2 + 1, i % 2 + 1)\n",
+                "\n",
+                "fig.update_yaxes(title_text=\"# Samples\", row=1, col=1)\n",
+                "fig.update_yaxes(title_text=\"# Samples\", row=2, col=1)\n",
+                "fig.update_xaxes(title_text=\"Distance\", row=2, col=1)\n",
+                "fig.update_xaxes(title_text=\"Distance\", row=2, col=2)\n",
+                "\n",
+                "# Overlay both histograms\n",
+                "fig.update_layout(height=700, hovermode=False, showlegend=False, margin=dict(l=20, r=20, t=50, b=20))\n",
+                "fig.update_yaxes(range=[0, 80])\n",
+                "fig.update_traces(opacity=0.7)\n",
+                "fig.show()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "## PDF Sampler\n",
+                "\n",
+                "The Probability Distribution Function (PDF) Sampler generates samples that match a given distribution. \n",
+                "\n",
+                "In the example below we first create a `UniformSampler` to generate a set of initial samples. We then assign weights to each of these samples to define the PDF (here it is an arbitrary funtion, but usually you would use the predicted weights from the field). The left plot the target PDF, on the right we plot a histogram of samples generated from the `PDFSampler`."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 2,
+            "metadata": {
+                "tags": [
+                    "hide-input"
+                ]
+            },
+            "outputs": [
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+                                    "title": {
+                                        "text": "# Samples"
+                                    }
+                                }
+                            }
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+                            "
                            
                    
"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
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+            ],
+            "source": [
+                "# COLLAPSED\n",
+                "import torch\n",
+                "from nerfstudio.cameras.rays import RayBundle\n",
+                "from nerfstudio.model_components import ray_sampler\n",
+                "import plotly.graph_objects as go\n",
+                "from plotly.subplots import make_subplots\n",
+                "\n",
+                "num_coarse_samples = 20\n",
+                "num_samples = 1000\n",
+                "near = 2\n",
+                "far = 5\n",
+                "train_stratified = False\n",
+                "\n",
+                "fig = make_subplots(\n",
+                "    rows=1,\n",
+                "    cols=2,\n",
+                "    subplot_titles=(\"PDF\", \"Samples\"),\n",
+                ")\n",
+                "\n",
+                "uniform_sampler = ray_sampler.UniformSampler(num_samples=num_coarse_samples, train_stratified=train_stratified)\n",
+                "pdf_sampler = ray_sampler.PDFSampler(num_samples=num_samples, train_stratified=train_stratified, include_original=False)\n",
+                "\n",
+                "ray_bundle = RayBundle(\n",
+                "    origins=torch.ones([1, 3]),\n",
+                "    directions=torch.ones([1, 3]),\n",
+                "    pixel_area=torch.ones([1, 1]),\n",
+                "    nears=torch.ones([1, 1]) * near,\n",
+                "    fars=torch.ones([1, 1]) * far,\n",
+                ")\n",
+                "\n",
+                "coarse_ray_samples = uniform_sampler(ray_bundle)\n",
+                "\n",
+                "# Generate arbitrary PDF\n",
+                "weights = torch.ones(num_coarse_samples)\n",
+                "weights += torch.sin(torch.linspace(0, 3 * torch.pi, num_coarse_samples))\n",
+                "weights += torch.sin(torch.linspace(0, 0.5 * torch.pi, num_coarse_samples))\n",
+                "weights -= torch.min(weights)\n",
+                "weights /= torch.sum(weights)\n",
+                "\n",
+                "samples = pdf_sampler.generate_ray_samples(ray_bundle, coarse_ray_samples, weights[None, :, None], num_samples)\n",
+                "\n",
+                "# Plotting stuff\n",
+                "x = torch.ones((num_coarse_samples * 2))\n",
+                "x[::2] = coarse_ray_samples.frustums.starts[0, :, 0]\n",
+                "x[1::2] = coarse_ray_samples.frustums.ends[0, :, 0]\n",
+                "\n",
+                "y = torch.ones((num_coarse_samples * 2))\n",
+                "y[::2] = weights\n",
+                "y[1::2] = weights\n",
+                "\n",
+                "pdf_trace = go.Scatter(x=x, y=y)\n",
+                "fig.append_trace(pdf_trace, 1, 1)\n",
+                "\n",
+                "samples_trace = go.Histogram(x=samples.frustums.starts[0, :, 0], nbinsx=100)\n",
+                "fig.append_trace(samples_trace, 1, 2)\n",
+                "\n",
+                "fig.update_yaxes(title_text=\"# Samples\", row=1, col=2)\n",
+                "fig.update_xaxes(title_text=\"Distance\", row=1, col=1)\n",
+                "fig.update_xaxes(title_text=\"Distance\", row=1, col=2)\n",
+                "\n",
+                "# Overlay both histograms\n",
+                "fig.update_layout(height=400, hovermode=False, showlegend=False, margin=dict(l=20, r=20, t=50, b=20))\n",
+                "fig.update_traces(opacity=0.7)\n",
+                "fig.show()"
+            ]
+        }
+    ],
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+        "kernelspec": {
+            "display_name": "Python 3",
+            "language": "python",
+            "name": "python3"
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"
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-   "source": [
-    "# COLLAPSED\n",
-    "import torch\n",
-    "from nerfstudio.cameras.rays import RayBundle\n",
-    "from nerfstudio.model_components import ray_sampler\n",
-    "import plotly.graph_objects as go\n",
-    "from plotly.subplots import make_subplots\n",
-    "\n",
-    "\n",
-    "num_samples = 1000\n",
-    "near = 2\n",
-    "far = 5\n",
-    "train_stratified = False\n",
-    "\n",
-    "samplers = [\n",
-    "    ray_sampler.UniformSampler,\n",
-    "    ray_sampler.LinearDisparitySampler,\n",
-    "    ray_sampler.SqrtSampler,\n",
-    "    ray_sampler.LogSampler,\n",
-    "]\n",
-    "\n",
-    "fig = make_subplots(\n",
-    "    rows=2,\n",
-    "    cols=2,\n",
-    "    subplot_titles=(\"Uniform\", \"Linear in Disparity\", \"Square Root\", \"Log Sampler\"),\n",
-    "    shared_xaxes=True,\n",
-    "    shared_yaxes=True,\n",
-    "    vertical_spacing=0.1,\n",
-    ")\n",
-    "\n",
-    "for i, Sampler in enumerate(samplers):\n",
-    "    sampler = Sampler(num_samples=num_samples, train_stratified=train_stratified)\n",
-    "\n",
-    "    ray_bundle = RayBundle(\n",
-    "        origins=torch.ones([1, 3]),\n",
-    "        directions=torch.ones([1, 3]),\n",
-    "        pixel_area=torch.ones([1, 1]),\n",
-    "        nears=torch.ones([1, 1]) * near,\n",
-    "        fars=torch.ones([1, 1]) * far,\n",
-    "    )\n",
-    "\n",
-    "    samples = sampler.generate_ray_samples(ray_bundle)\n",
-    "\n",
-    "    trace = go.Histogram(x=samples.frustums.starts[0, :, 0], nbinsx=50)\n",
-    "    fig.append_trace(trace, i // 2 + 1, i % 2 + 1)\n",
-    "\n",
-    "fig.update_yaxes(title_text=\"# Samples\", row=1, col=1)\n",
-    "fig.update_yaxes(title_text=\"# Samples\", row=2, col=1)\n",
-    "fig.update_xaxes(title_text=\"Distance\", row=2, col=1)\n",
-    "fig.update_xaxes(title_text=\"Distance\", row=2, col=2)\n",
-    "\n",
-    "# Overlay both histograms\n",
-    "fig.update_layout(height=700, hovermode=False, showlegend=False, margin=dict(l=20, r=20, t=50, b=20))\n",
-    "fig.update_yaxes(range=[0, 80])\n",
-    "fig.update_traces(opacity=0.7)\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## PDF Sampler\n",
-    "\n",
-    "The Probability Distribution Function (PDF) Sampler generates samples that match a given distribution. \n",
-    "\n",
-    "In the example below we first create a `UniformSampler` to generate a set of initial samples. We then assign weights to each of these samples to define the PDF (here it is an arbitrary function, but usually you would use the predicted weights from the field). The left plot the target PDF, on the right we plot a histogram of samples generated from the `PDFSampler`."
-   ]
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"
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-   "source": [
-    "# COLLAPSED\n",
-    "import torch\n",
-    "from nerfstudio.cameras.rays import RayBundle\n",
-    "from nerfstudio.model_components import ray_sampler\n",
-    "import plotly.graph_objects as go\n",
-    "from plotly.subplots import make_subplots\n",
-    "\n",
-    "num_coarse_samples = 20\n",
-    "num_samples = 1000\n",
-    "near = 2\n",
-    "far = 5\n",
-    "train_stratified = False\n",
-    "\n",
-    "fig = make_subplots(\n",
-    "    rows=1,\n",
-    "    cols=2,\n",
-    "    subplot_titles=(\"PDF\", \"Samples\"),\n",
-    ")\n",
-    "\n",
-    "uniform_sampler = ray_sampler.UniformSampler(num_samples=num_coarse_samples, train_stratified=train_stratified)\n",
-    "pdf_sampler = ray_sampler.PDFSampler(num_samples=num_samples, train_stratified=train_stratified, include_original=False)\n",
-    "\n",
-    "ray_bundle = RayBundle(\n",
-    "    origins=torch.ones([1, 3]),\n",
-    "    directions=torch.ones([1, 3]),\n",
-    "    pixel_area=torch.ones([1, 1]),\n",
-    "    nears=torch.ones([1, 1]) * near,\n",
-    "    fars=torch.ones([1, 1]) * far,\n",
-    ")\n",
-    "\n",
-    "coarse_ray_samples = uniform_sampler(ray_bundle)\n",
-    "\n",
-    "# Generate arbitrary PDF\n",
-    "weights = torch.ones(num_coarse_samples)\n",
-    "weights += torch.sin(torch.linspace(0, 3 * torch.pi, num_coarse_samples))\n",
-    "weights += torch.sin(torch.linspace(0, 0.5 * torch.pi, num_coarse_samples))\n",
-    "weights -= torch.min(weights)\n",
-    "weights /= torch.sum(weights)\n",
-    "\n",
-    "samples = pdf_sampler.generate_ray_samples(ray_bundle, coarse_ray_samples, weights[None, :, None], num_samples)\n",
-    "\n",
-    "# Plotting stuff\n",
-    "x = torch.ones((num_coarse_samples * 2))\n",
-    "x[::2] = coarse_ray_samples.frustums.starts[0, :, 0]\n",
-    "x[1::2] = coarse_ray_samples.frustums.ends[0, :, 0]\n",
-    "\n",
-    "y = torch.ones((num_coarse_samples * 2))\n",
-    "y[::2] = weights\n",
-    "y[1::2] = weights\n",
-    "\n",
-    "pdf_trace = go.Scatter(x=x, y=y)\n",
-    "fig.append_trace(pdf_trace, 1, 1)\n",
-    "\n",
-    "samples_trace = go.Histogram(x=samples.frustums.starts[0, :, 0], nbinsx=100)\n",
-    "fig.append_trace(samples_trace, 1, 2)\n",
-    "\n",
-    "fig.update_yaxes(title_text=\"# Samples\", row=1, col=2)\n",
-    "fig.update_xaxes(title_text=\"Distance\", row=1, col=1)\n",
-    "fig.update_xaxes(title_text=\"Distance\", row=1, col=2)\n",
-    "\n",
-    "# Overlay both histograms\n",
-    "fig.update_layout(height=400, hovermode=False, showlegend=False, margin=dict(l=20, r=20, t=50, b=20))\n",
-    "fig.update_traces(opacity=0.7)\n",
-    "fig.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.7"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+    "nbformat": 4,
+    "nbformat_minor": 2
 }
diff --git a/docs/quickstart/custom_dataset.md b/docs/quickstart/custom_dataset.md
index ebd77a5f5..65e6c22d4 100644
--- a/docs/quickstart/custom_dataset.md
+++ b/docs/quickstart/custom_dataset.md
@@ -259,8 +259,6 @@ ns-train nerfacto --data {output directory}
 
 ## Metashape
 
-All images must use the same sensor type (but multiple sensors are supported).
-
 1. Align your images using Metashape. `File -> Workflow -> Align Photos...`
 
 ```{image} https://user-images.githubusercontent.com/3310961/203389662-12760210-2b52-49d4-ab21-4f23bfa4a2b3.png
@@ -343,6 +341,4 @@ The `crop-factor` argument is optional but often very helpful. This is because e
 
   
 
-The pixels representing the distorted hand and head are obviously not useful in training a nerf so we can remove it by cropping the bottom 20% of the image. This can be done by using the `--crop-factor 0 0.2 0 0` argument.
-
-If cropping only needs to be done from the bottom, you can use the `--crop-bottom [num]` argument which would be the same as doing `--crop-factor 0.0 [num] 0.0 0.0`
+This obscene mesh of human is obviously not helpful in training a nerf so we can remove it by cropping the bottom 20% of the image. This can be done by using the `--crop-factor 0 0.2 0 0` argument.
diff --git a/docs/quickstart/export_geometry.md b/docs/quickstart/export_geometry.md
index 2ffd51895..28d29fc54 100644
--- a/docs/quickstart/export_geometry.md
+++ b/docs/quickstart/export_geometry.md
@@ -19,6 +19,7 @@ Poisson surface reconstruction gives the highest quality meshes. See the steps b
 > **Note:**
 > This will only work with a Model that computes or predicts normals, e.g., nerfacto.
 
+
 1. Train nerfacto with network settings that predict normals.
 
 ```bash
@@ -50,7 +51,7 @@ ns-export --help
 Say you want to simplify and/or smooth a mesh offline, and then you want to texture it with NeRF. You can do that with the following command. It will work for any mesh filetypes that [PyMeshLab](https://pymeshlab.readthedocs.io/en/latest/) can support, for example a `.ply`.
 
 ```python
-python nerfstudio/scripts/texture.py --load-config CONFIG.yml --input-mesh-filename FILENAME --output-dir OUTPUT_DIR
+python scripts/texture.py --load-config CONFIG.yml --input-mesh-filename FILENAME --output-dir OUTPUT_DIR
 ```
 
 ## Dependencies
diff --git a/docs/quickstart/first_nerf.md b/docs/quickstart/first_nerf.md
index 1d71fac49..73e40a9d8 100644
--- a/docs/quickstart/first_nerf.md
+++ b/docs/quickstart/first_nerf.md
@@ -38,6 +38,8 @@ It is possible to load a pretrained model by running
 ns-train nerfacto --data data/nerfstudio/poster --load-dir {outputs/.../nerfstudio_models}
 ```
 
+This will automatically start training. If you do not want it to train, add `--viewer.start-train False` to your training command.
+
 ## Visualize existing run
 
 Given a pretrained model checkpoint, you can start the viewer by running
@@ -96,9 +98,9 @@ ns-train nerfacto  nerfstudio-data --help
 
 Each script will have some other minor quirks (like the training script dataparser subcommand needing to come after the model subcommand), read up on them [here](../reference/cli/index.md).
 
-## Tensorboard / WandB / Viewer
+## Tensorboard / WandB
 
-We support three different methods to track training progress, using the viewer, [tensorboard](https://www.tensorflow.org/tensorboard), and [Weights and Biases](https://wandb.ai/site). You can specify which visualizer to use by appending `--vis {viewer, tensorboard, wandb, viewer+wandb, viewer+tensorboard}` to the training command. Simultaneously utilizing the viewer alongside wandb or tensorboard may cause stuttering issues during evaluation steps. The viewer only works for methods that are fast (ie. nerfacto, instant-ngp), for slower methods like NeRF, use the other loggers.
+We support three different methods to track training progress, using the viewer, [tensorboard](https://www.tensorflow.org/tensorboard), and [Weights and Biases](https://wandb.ai/site). You can specify which visualizer to use by appending `--vis {viewer, tensorboard, wandb}` to the training command. Note that only one may be used at a time. Additionally the viewer only works for methods that are fast (ie. nerfacto, instant-ngp), for slower methods like NeRF, use the other loggers.
 
 ## Evaluating Runs
 
diff --git a/docs/quickstart/installation.md b/docs/quickstart/installation.md
index 58f9ffe1d..dafb9f0ee 100644
--- a/docs/quickstart/installation.md
+++ b/docs/quickstart/installation.md
@@ -33,7 +33,7 @@ Install CUDA 11.7. You can find CUDA download links [here](https://developer.nvi
 
 ## Create environment
 
-Nerfstudio requires `python >= 3.8`. We recommend using conda to manage dependencies. Make sure to install [Conda](https://docs.conda.io/en/latest/miniconda.html) before proceeding.
+Nerfstudio requires `python >= 3.7`. We recommend using conda to manage dependencies. Make sure to install [Conda](https://docs.conda.io/en/latest/miniconda.html) before proceeding.
 
 ```bash
 conda create --name nerfstudio -y python=3.8
diff --git a/docs/reference/api/data/datamanagers.rst b/docs/reference/api/data/datamanagers.rst
index 43e5d6cce..7779c9e10 100644
--- a/docs/reference/api/data/datamanagers.rst
+++ b/docs/reference/api/data/datamanagers.rst
@@ -7,4 +7,25 @@ Base
 ----------------
 
 .. automodule:: nerfstudio.data.datamanagers.base_datamanager
-   :members:
\ No newline at end of file
+   :members:
+
+Depth Datamanager
+-----------------
+
+.. automodule:: nerfstudio.data.datamanagers.depth_datamanager
+   :members:
+   :show-inheritance:
+
+SDF Datamanager
+----------------
+
+.. automodule:: nerfstudio.data.datamanagers.sdf_datamanager
+   :members:
+   :show-inheritance:
+
+Semantic Datamanager
+---------------------
+
+.. automodule:: nerfstudio.data.datamanagers.semantic_datamanager
+   :members:
+   :show-inheritance:
\ No newline at end of file
diff --git a/docs/reference/cli/index.md b/docs/reference/cli/index.md
index 4daee6555..7bc95834f 100644
--- a/docs/reference/cli/index.md
+++ b/docs/reference/cli/index.md
@@ -6,16 +6,16 @@ We provide a command line interface for training your own NeRFs (no coding neces
 
 Here are the popular commands that we offer. If you've cloned the repo, you can also look at the [pyproject.toml file](https://github.com/nerfstudio-project/nerfstudio/blob/main/pyproject.toml) at the `[project.scripts]` section for details.
 
-| Command                              | Description                            | Filename                                      |
-| ------------------------------------ | -------------------------------------- | --------------------------------------------- |
-| [ns-install-cli](ns_install_cli)     | Install tab completion for all scripts | nerfstudio/scripts/completions/install.py     |
-| [ns-process-data](ns_process_data)   | Generate a dataset from your own data  | nerfstudio/scripts/process_data.py            |
-| [ns-download-data](ns_download_data) | Download existing captures             | nerfstudio/scripts/downloads/download_data.py |
-| [ns-train](ns_train)                 | Generate a NeRF                        | nerfstudio/scripts/train.py                   |
-| [ns-viewer](ns_viewer)               | View a trained NeRF                    | nerfstudio/scripts/viewer/run_viewer.py       |
-| [ns-eval](ns_eval)                   | Run evaluation metrics for your Model  | nerfstudio/scripts/eval.py                    |
-| [ns-render](ns_render)               | Render out a video of your NeRF        | nerfstudio/scripts/render.py                  |
-| [ns-export](ns_export)               | Export a NeRF into other formats       | nerfstudio/scripts/exporter.py                |
+| Command                              | Description                            | Filename                           |
+| ------------------------------------ | -------------------------------------- | ---------------------------------- |
+| [ns-install-cli](ns_install_cli)     | Install tab completion for all scripts | scripts/completions/install.py     |
+| [ns-process-data](ns_process_data)   | Generate a dataset from your own data  | scripts/process_data.py            |
+| [ns-download-data](ns_download_data) | Download existing captures             | scripts/downloads/download_data.py |
+| [ns-train](ns_train)                 | Generate a NeRF                        | scripts/train.py                   |
+| [ns-viewer](ns_viewer)               | View a trained NeRF                    | scripts/viewer/run_viewer.py       |
+| [ns-eval](ns_eval)                   | Run evaluation metrics for your Model  | scripts/eval.py                    |
+| [ns-render](ns_render)               | Render out a video of your NeRF        | scripts/render.py                  |
+| [ns-export](ns_export)               | Export a NeRF into other formats       | scripts/exporter.py                |
 
 ```{toctree}
 :maxdepth: 1
diff --git a/docs/reference/cli/ns_download_data.md b/docs/reference/cli/ns_download_data.md
index 1105ef12b..d096b4e07 100644
--- a/docs/reference/cli/ns_download_data.md
+++ b/docs/reference/cli/ns_download_data.md
@@ -2,7 +2,7 @@
 
 ```{eval-rst}
 .. argparse::
-    :module: nerfstudio.scripts.downloads.download_data
+    :module: scripts.downloads.download_data
     :func: get_parser_fn
     :prog: ns-download-data
     :nodefault:
diff --git a/docs/reference/cli/ns_eval.md b/docs/reference/cli/ns_eval.md
index 86e4aa0ba..a794ac9e8 100644
--- a/docs/reference/cli/ns_eval.md
+++ b/docs/reference/cli/ns_eval.md
@@ -2,7 +2,7 @@
 
 ```{eval-rst}
 .. argparse::
-    :module: nerfstudio.scripts.eval
+    :module: scripts.eval
     :func: get_parser_fn
     :prog: ns-eval
     :nodefault:
diff --git a/docs/reference/cli/ns_export.md b/docs/reference/cli/ns_export.md
index 839211915..cdfc54f2f 100644
--- a/docs/reference/cli/ns_export.md
+++ b/docs/reference/cli/ns_export.md
@@ -2,7 +2,7 @@
 
 ```{eval-rst}
 .. argparse::
-    :module: nerfstudio.scripts.exporter
+    :module: scripts.exporter
     :func: get_parser_fn
     :prog: ns-export
     :nodefault:
diff --git a/docs/reference/cli/ns_install_cli.md b/docs/reference/cli/ns_install_cli.md
index 30fa9598f..e9c1a9541 100644
--- a/docs/reference/cli/ns_install_cli.md
+++ b/docs/reference/cli/ns_install_cli.md
@@ -2,7 +2,7 @@
 
 ```{eval-rst}
 .. argparse::
-    :module: nerfstudio.scripts.completions.install
+    :module: scripts.completions.install
     :func: get_parser_fn
     :prog: ns-install-cli
     :nodefault:
diff --git a/docs/reference/cli/ns_process_data.md b/docs/reference/cli/ns_process_data.md
index 9f9540d44..bb5bd24ac 100644
--- a/docs/reference/cli/ns_process_data.md
+++ b/docs/reference/cli/ns_process_data.md
@@ -8,7 +8,7 @@ You may also want to install [hloc](https://github.com/cvg/Hierarchical-Localiza
 
 ```{eval-rst}
 .. argparse::
-    :module: nerfstudio.scripts.process_data
+    :module: scripts.process_data
     :func: get_parser_fn
     :prog: ns-process-data
     :nodefault:
diff --git a/docs/reference/cli/ns_render.md b/docs/reference/cli/ns_render.md
index e006e62ee..7a01f9bae 100644
--- a/docs/reference/cli/ns_render.md
+++ b/docs/reference/cli/ns_render.md
@@ -7,7 +7,7 @@ Make sure to have [FFmpeg](https://ffmpeg.org/download.html) installed.
 
 ```{eval-rst}
 .. argparse::
-    :module: nerfstudio.scripts.render
+    :module: scripts.render
     :func: get_parser_fn
     :prog: ns-render
     :nodefault:
diff --git a/docs/reference/cli/ns_viewer.md b/docs/reference/cli/ns_viewer.md
index bb6333550..841472a5f 100644
--- a/docs/reference/cli/ns_viewer.md
+++ b/docs/reference/cli/ns_viewer.md
@@ -2,7 +2,7 @@
 
 ```{eval-rst}
 .. argparse::
-    :module: nerfstudio.scripts.viewer.run_viewer
+    :module: scripts.viewer.run_viewer
     :func: get_parser_fn
     :prog: ns-viewer
     :nodefault:
diff --git a/docs/reference/contributing.md b/docs/reference/contributing.md
index 101cb873e..a9651cbed 100644
--- a/docs/reference/contributing.md
+++ b/docs/reference/contributing.md
@@ -7,14 +7,13 @@ Below, we document the contribution pipeline and good-to-knows for when you're r
 The project is set up for development in VSCode, we recommend using it if you plan to contribute.
 
 ## Overview
-
 Below are the various tooling features our team uses to maintain this codebase.
 
-| Tooling         | Support                                                    |
-| --------------- | ---------------------------------------------------------- |
-| Linter          | [Black](https://black.readthedocs.io/en/stable/)           |
-| Testing         | [PyTest](https://docs.pytest.org/en/7.1.x/)                |
-| Docs            | [Sphinx](https://www.sphinx-doc.org/en/master/)            |
+|    Tooling      |    Support    |
+| --------------- | ------------- |
+| Linter          | [Black](https://black.readthedocs.io/en/stable/)  |
+| Testing         | [PyTest](https://docs.pytest.org/en/7.1.x/) |
+| Docs            | [Sphinx](https://www.sphinx-doc.org/en/master/) |
 | Docstring style | [Google](https://google.github.io/styleguide/pyguide.html) |
 
 ## Requirements
@@ -54,8 +53,8 @@ conda install -c conda-forge pandoc
 
    :::{admonition} Attention
    :class: attention
-   In order to merge changes to the code base, all of these checks must be passing. If you pass these tests locally, you will likely pass on github servers as well (results in a green checkmark next to your commit).
-   :::
+      In order to merge changes to the code base, all of these checks must be passing. If you pass these tests locally, you will likely pass on github servers as well (results in a green checkmark next to your commit).
+      :::
 
 3. Open pull request! 💌
 
@@ -66,7 +65,7 @@ conda install -c conda-forge pandoc
 Run the following to build the documentation:
 
 ```bash
-python nerfstudio/scripts/docs/build_docs.py
+python scripts/docs/build_docs.py
 ```
 
 :::{admonition} Tip
@@ -85,7 +84,7 @@ If you want the code to build on save you can use [sphinx autobuild](https://git
 :class: info
 
 If changes to the structure are made, the build files may be incorrect.
-:::
+  :::
 
 ```bash
 pip install sphinx-autobuild