* Allow black/white background color in Nerfacto
* Use white background in Nerfacto Blender Benchmark
* Fixed unused import
* Updated index for license headers script
* fixed an exception bug in case of num_samples is 0
fixed an exception bug when num samples is 0 ,
"IndexError tensors used as indices must be long, byte or bool tensors"
change type to long ( was float32)
* implemented crop box in gui. For now works on instant-ngp-bounded
* fixed import bugs
* update aabb every time and not only on training
* prevent crash when chosing accomulation and crop box
* Checkbox
* max_box is bounded by scevne box in the gui
* implemented altenative intesection to nerfacc
* convert str to bool correctly
* fixed isort bug
* fixed isort bug2
* update viewer version
* remove json bug
* Update UI
* Force black background
* Fix formatting
Co-authored-by: dror <you@example.com>
Co-authored-by: Matt Tancik <tancik@berkeley.edu>
Co-authored-by: Matthew Tancik <mattt4001@gmail.com>
* fix notebook
* samples
* run all cells
* box center
* try saving again
* import order
* cube with different sides
* black
* black
* update metadata
* fix test
Co-authored-by: Katrin Schmid <kabischmid@gmail.com>
Co-authored-by: Matt Tancik <tancik@berkeley.edu>
Making TrainerConfig an InstantiateConfig so it can be swapped in and out with different methods. Also made the TrainerConfig a subclass of ExperimentConfig (which was changed from a printableconfig to an instantiateconfig), since we were already instantiating the Trainer class with the ExperimentConfig
Removed the "config" field from the TrainerCallbackAttributes struct to eliminate circular dependencies when types are checked.
Minor scripts and docs changes to accommodate the update to the config struct, meaning the flags that were associated with the TrainerConfig were moved up to the top level of the config.
* inerted a new model configuration instant-ngp-bounded
* added more remarks and small fixes
* went back to old near plane
* added the oprtion to get a file as well as directory in nerfstudio data parser
* pretify prev commit
* review fixes
* more clear explanation in README.md
* reverted readme changes
* fixed merge bug
* review changes. update config.data docs support for json files inputs
Co-authored-by: dror <you@example.com>
This branch gives us the flexibility to use Cameras as a tensordataclass, giving arbitrary batch dimensions, slicing, etc all for free. It tests this with the phototourism dataset.
Tensordataclass changes:
Originally, the tensordataclass would look at tensors in an object and take everything in front of the last dimension to be the batch dimensions, but in the case of the camera_to_world matrices, everything except for the last TWO are the important dimensions, so we needed to have a way for the tensordataclass to acknowledge these specific fields where there are multiple data dimensions behind the batch dimensions. This is important for things like reshaping where we need to know which dimensions get reshaped and which dimensions get left alone.
Cameras changes:
Now subclass of tensordataclass, which required a change to the tensordataclass itself.
The possibility of extra batch dimensions is the hairy part that requires the majority of these changes. The extra batch dimensions aren't a hassle in every function except in ray generation. The ray generation is where this operates different relative to the old cameras. The reason for this is before, for each ray we wanted to generate, we could specify a single index into our batch of cameras, but if you have multiple camera dimensions, for each ray, you need to now specify one index per camera batch dimension, meaning our camera_indices we specify need to have one extra dimension at the end, a dimension of size Cameras.ndim (number of batch dimensions in the camera object).
I also added some neat logic in generate_rays where if you want your output raybundle to be batched, if you specify batched camera_indices, you will receive the same batch size for the output raybundle, meaning you can easily generate raybundles corresponding to things like image patches and whatnot.
Phototourism changes:
The difficulty here is that images in the phototourism dataset are of different sizes, which is difficult because we can no longer batch the image tensors. We also need to better utilize caching in the CacheDataloader since the vanilladatamanager defaults don't really use it as well as they could, and without properly utilizing it, the rays / sec are maybe 25% of the standard nerfstudio dataset speeds (which are 10% of the size of the phototourism datasets). To do this, we specify a collate function in the existing datamanager configs, and we make a subclass of the vanilladatamanagerconfig named VariableResDataManagerConfig which will have a custom collate function storing the images in a list, and using better defaults for caching in the dataloader, caching ~40 images at a time. The slowest part of using a list is pixel sampling, where we added a custom if case for if our pixel sampler receives a batch of images that are in a list, and the lack of ability to vectorize operations over a list means that caching in the dataloader is even more important.
* Add D-NeRF temporal distortion and D-NeRF model
This allows for temporal distortion to be enabled on a variety of models through config
parameters, without an explicit model. Temporal distortion and MIP-NeRF are not currently
jointly runnable but can be changed in the future.
* Simplifying changes
* Simplifying changes
Co-authored-by: Matthew Tancik <tancik@berkeley.edu>
* misc cuda improvements. added cone angle as param
* wandb as default. better organization for experiments
* turn off tensorboard
* disable eval when using viewer
* remove comment
* launch.json
* testing; need to merge
* fixing wrong base file in models/base
* pushed input parser into datasets class to abstract away fetching data in dataloader
* slight name chage and all
* nits
* added dataparser
* added licnese
