Merge branch 'main' into cmk/ns_add_plotly

docs/index.md

@@ -147,6 +147,7 @@ This documentation is organized into 3 parts:
 
 ### Third-party Methods
 
+- [BioNeRF](nerfology/methods/bionerf.md): Biologically Plausible Neural Radiance Fields for View Synthesis
 - [Instruct-NeRF2NeRF](nerfology/methods/in2n.md): Editing 3D Scenes with Instructions
 - [Instruct-GS2GS](nerfology/methods/igs2gs.md): Editing 3DGS Scenes with Instructions
 - [SIGNeRF](nerfology/methods/signerf.md): Controlled Generative Editing of NeRF Scenes

docs/nerfology/methods/bionerf.md

@@ -0,0 +1,86 @@
+# BioNeRF
+
+<h4> Biologically Plausible Neural Radiance Fields for View Synthesis</h4>
+
+```{button-link} https://arxiv.org/pdf/2402.07310
+:color: primary
+:outline:
+Paper Website
+```
+
+```{button-link} https://github.com/Leandropassosjr/BioNeRF
+:color: primary
+:outline:
+Official Code
+```
+
+```{button-link} https://github.com/Leandropassosjr/ns_bionerf
+:color: primary
+:outline:
+Nerfstudio add-on code
+```
+![examples](https://leandropassosjr.github.io/BioNeRF/assets/gifs.gif)<br>
+
+**Biologically Plausible Neural Radiance Fields**
+
+## Installation
+
+Install nerfstudio dependencies.
+
+## Running BioNeRF
+
+Once the nerfstudio dependencies are installed, run:
+
+```bash
+ns-train bionerf --help
+```
+
+## Method
+
+### Overview
+
+[BioNeRF](https://arxiv.org/pdf/2402.07310.pdf) (Biologically Plausible Neural Radiance Fields) extends [NeRF](http://www.matthewtancik.com/nerf) by implementing a cognitive-inspired mechanism that fuses inputs from multiple sources into a memory-like structure, thus improving the storing capacity and extracting more intrinsic and correlated information. BioNeRF also mimics a behavior observed in pyramidal cells concerning contextual information, in which the memory is provided as the context and combined with the inputs of two subsequent blocks of dense layers, one responsible for producing the volumetric densities and the other the colors used to render the novel view.
+
+## Pipeline
+
+![pipeline](https://leandropassosjr.github.io/BioNeRF/assets/BioNeRF.png)<br>
+
+Here is an overview pipeline for BioNeRF, we will walk through each component in this guide.
+
+### Positional Feature Extraction
+The first step consists of feeding two neural models simultaneously, namely $M_{\Delta}$ and $M_c$, with the camera positional information. The output of these models encodes the positional information from the input image. Although the input is the same, the neural models do not share weights and follow a different flow in the next steps.
+
+### Cognitive Filtering
+This step performs a series of operations, called *filters*, that work on the embeddings coming from the previous step. There are four filters this step derives: density, color, memory, and modulation.
+
+### Memory Updating
+Updating the memory requires the implementation of a mechanism capable of obliterating trivial information, which is performed using the memory filter (Step 3.1 in the figure). Fist, one needs to compute a signal modulation **$\mu$**, for further introducing new experiences in the memory **$\Psi$** through the modulating variable **$\mu$** using a $\textit{tanh}$ function (Step 3.2 in the figure).
+
+### Contextual Inference
+This step is responsible for adding contextual information to BioNeRF. Two new embeddings are generated, i.e., **${h}^{\prime}_\Delta$** and **${h}^{\prime}_c$** based on density and color filters, respectively (Step 4 in the figure), which further feed two neural models, i.e., $M^\prime_\Delta$ and $M^\prime_c$. Subsequently, $M^\prime_\Delta$ outputs the volume density, while color information is predicted by $M^\prime_c$, further used to compute the final predicted pixel information and the loss function.
+
+## Results
+
+**Blender** (synthetic)
+
+|       | drums | materials | ficus | ship  | mic   | chair | lego  | hotdog | AVG   |
+| ----- | ----- | --------- | ----- | ----  | ---   | ----- | ----  | ------ | ---   |
+| PSNR  | 25.66 | 29.74     | 29.56 | 29.57 | 33.38 | 34.63 | 31.82 | 37.23  | 31.45 |
+| SSIM  | 0.927 | 0.957     | 0.965 | 0.874 | 0.978 | 0.977 | 0.963 | 0.980  | 0.953 |
+| LPIPS | 0.047 | 0.018     | 0.017 | 0.068 | 0.018 | 0.011 | 0.016 | 0.010  | 0.026 |
+
+<img src='https://leandropassosjr.github.io/BioNeRF/assets/blender.jpeg'/>
+
+Ground-truth (top) and synthetic view (bottom) images generated by BioNeRF regarding four Realistic Blender dataset’s scenes.
+
+**LLFF** (real)
+
+|       | Fern  | Flower | Fortress | Horns  | Leaves | Orchids  | Room  | T-Rex | AVG   |
+| ----  | ----- | ------ | -------- | -----  | -----  | -------  | ----- | ----- | ---   |
+| PSNR  | 25.17 | 27.89  | 32.34    | 27.99  | 22.23  | 20.80    | 30.75 | 27.56 | 27.01 |
+| SSIM  | 0.837 | 0.873  | 0.914    | 0.882  | 0.796  | 0.714    | 0.911 | 0.911 | 0.861 |
+| LPIPS | 0.093 | 0.055  | 0.025    | 0.070  | 0.103  | 0.122    | 0.029 | 0.044 | 0.068 |
+
+<img src='https://leandropassosjr.github.io/BioNeRF/assets/llff.jpeg'/>
+
+Ground-truth (top) and synthetic view (bottom) images generated by BioNeRF regarding four LLFF dataset’s scenes.

docs/nerfology/methods/index.md

@@ -44,6 +44,7 @@ The following methods are supported in nerfstudio:
     PyNeRF<pynerf.md>
     SeaThru-NeRF<seathru_nerf.md>
     Zip-NeRF<zipnerf.md>
+    BioNeRF<bionerf.md>
 ```
 
 (own_method_docs)=

nerfstudio/configs/external_methods.py

@@ -141,6 +141,19 @@ To enable Volinga, you must install it first by running:
     )
 )
 
+# BioNeRF
+external_methods.append(
+    ExternalMethod(
+        """[bold yellow]BioNeRF[/bold yellow]
+For more information visit: https://docs.nerf.studio/nerfology/methods/bionerf.html
+
+To enable BioNeRF, you must install it first by running:
+  [grey]pip install git+https://github.com/Leandropassosjr/ns_bionerf[/grey]""",
+        configurations=[("BioNeRF", "BioNeRF. Nerfstudio implementation")],
+        pip_package="git+https://github.com/Leandropassosjr/ns_bionerf",
+    )
+)
+
 # Instruct-GS2GS
 external_methods.append(
     ExternalMethod(

nerfstudio/process_data/polycam_utils.py

@@ -17,7 +17,9 @@
 import json
 import sys
 from pathlib import Path
-from typing import List, Tuple
+from typing import List, Optional, Tuple
+
+import numpy as np
 
 from nerfstudio.process_data import process_data_utils
 from nerfstudio.process_data.process_data_utils import CAMERA_MODELS
@@ -28,6 +30,7 @@ from nerfstudio.utils.rich_utils import CONSOLE
 def polycam_to_json(
     image_filenames: List[Path],
     depth_filenames: List[Path],
+    glb_filename: Optional[Path],
     cameras_dir: Path,
     output_dir: Path,
     min_blur_score: float = 0.0,
@@ -80,6 +83,31 @@ def polycam_to_json(
         frames.append(frame)
     data["frames"] = frames
 
+    if glb_filename is not None:
+        # If the .glb is populated, use it to save a pointcloud for splatfacto init
+        import open3d as o3d
+
+        mesh = o3d.io.read_triangle_mesh(str(glb_filename), enable_post_processing=True)
+        textures = np.asarray(mesh.textures[0])  # 2D images of color
+        vert_points = np.asarray(mesh.vertices)  # 3D positions of verts
+        tri_ids = np.asarray(mesh.triangles)  # indices of the vertices
+        points = vert_points[tri_ids.flatten()]  # get the 3D positions of the vertices
+        uvs = np.asarray(mesh.triangle_uvs)  # get the uv coords of the vertices
+        # convert uv coord to texture integer index
+        tex_ids = (uvs[:, 1] * textures.shape[0]).astype(int), (uvs[:, 0] * textures.shape[1]).astype(int)
+        colors = textures[tex_ids[0], tex_ids[1]]
+        pointcloud = o3d.geometry.PointCloud(o3d.utility.Vector3dVector(points))
+        pointcloud.colors = o3d.utility.Vector3dVector(colors.astype(np.float64) / 255.0)
+        pointcloud = pointcloud.remove_duplicated_points()
+        # align the pointcloud to the coord system of cameras, which is provided inside the mesh_info.json file
+        mesh_info_json = json.load(open(glb_filename.parent / "mesh_info.json"))
+        transform = np.array(mesh_info_json["alignmentTransform"]).reshape(4, 4).T
+        pointcloud = pointcloud.transform(np.linalg.inv(transform))
+        # shift the axes coordinates to match the nerfstudio ones (same as the cameras' coord system)
+        pointcloud.points = o3d.utility.Vector3dVector(np.array(pointcloud.points)[:, [2, 0, 1]])
+        o3d.io.write_point_cloud(str(output_dir / "point_cloud.ply"), pointcloud)
+        data["ply_file_path"] = "point_cloud.ply"
+
     with open(output_dir / "transforms.json", "w", encoding="utf-8") as f:
         json.dump(data, f, indent=4)
 

nerfstudio/scripts/process_data.py

@@ -200,6 +200,7 @@ class ProcessPolycam(BaseConverterToNerfstudioDataset):
             polycam_utils.polycam_to_json(
                 image_filenames=polycam_image_filenames,
                 depth_filenames=polycam_depth_filenames,
+                glb_filename=self.data / "raw.glb" if (self.data / "raw.glb").exists() else None,
                 cameras_dir=polycam_cameras_dir,
                 output_dir=self.output_dir,
                 min_blur_score=self.min_blur_score,