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DirectVoxGO

Direct Voxel Grid Optimization (CVPR2022 Oral, project page, DVGO paper, DVGO v2 paper).

https://user-images.githubusercontent.com/2712505/153380311-19d6c3a1-9130-489a-af16-ad36c78f10a9.mp4

https://user-images.githubusercontent.com/2712505/153380197-991d1689-6418-499c-a192-d757f9a64b64.mp4

Custom casual capturing

A short guide to capture custom forward-facing scenes and rendering fly-through videos.

Below are two rgb and depth fly-through videos from custom captured scenes.

https://user-images.githubusercontent.com/2712505/174267754-619d4f81-dd04-4c50-ba7f-434774cb890e.mp4

Features

Installation

git clone git@github.com:sunset1995/DirectVoxGO.git
cd DirectVoxGO
pip install -r requirements.txt

Pytorch and torch_scatter installation is machine dependent, please install the correct version for your machine.

<details> <summary> Dependencies (click to expand) </summary> </details>

Directory structure for the datasets

<details> <summary> (click to expand;) </summary> 
data
├── nerf_synthetic     # Link: https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1
│   └── [chair|drums|ficus|hotdog|lego|materials|mic|ship]
│       ├── [train|val|test]
│       │   └── r_*.png
│       └── transforms_[train|val|test].json
│
├── Synthetic_NSVF     # Link: https://dl.fbaipublicfiles.com/nsvf/dataset/Synthetic_NSVF.zip
│   └── [Bike|Lifestyle|Palace|Robot|Spaceship|Steamtrain|Toad|Wineholder]
│       ├── intrinsics.txt
│       ├── rgb
│       │   └── [0_train|1_val|2_test]_*.png
│       └── pose
│           └── [0_train|1_val|2_test]_*.txt
│
├── BlendedMVS         # Link: https://dl.fbaipublicfiles.com/nsvf/dataset/BlendedMVS.zip
│   └── [Character|Fountain|Jade|Statues]
│       ├── intrinsics.txt
│       ├── rgb
│       │   └── [0|1|2]_*.png
│       └── pose
│           └── [0|1|2]_*.txt
│
├── TanksAndTemple     # Link: https://dl.fbaipublicfiles.com/nsvf/dataset/TanksAndTemple.zip
│   └── [Barn|Caterpillar|Family|Ignatius|Truck]
│       ├── intrinsics.txt
│       ├── rgb
│       │   └── [0|1|2]_*.png
│       └── pose
│           └── [0|1|2]_*.txt
│
├── deepvoxels         # Link: https://drive.google.com/drive/folders/1ScsRlnzy9Bd_n-xw83SP-0t548v63mPH
│   └── [train|validation|test]
│       └── [armchair|cube|greek|vase]
│           ├── intrinsics.txt
│           ├── rgb/*.png
│           └── pose/*.txt
│
├── nerf_llff_data     # Link: https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1
│   └── [fern|flower|fortress|horns|leaves|orchids|room|trex]
│
├── tanks_and_temples  # Link: https://drive.google.com/file/d/11KRfN91W1AxAW6lOFs4EeYDbeoQZCi87/view?usp=sharing
│   └── [tat_intermediate_M60|tat_intermediate_Playground|tat_intermediate_Train|tat_training_Truck]
│       └── [train|test]
│           ├── intrinsics/*txt
│           ├── pose/*txt
│           └── rgb/*jpg
│
├── lf_data            # Link: https://drive.google.com/file/d/1gsjDjkbTh4GAR9fFqlIDZ__qR9NYTURQ/view?usp=sharing
│   └── [africa|basket|ship|statue|torch]
│       └── [train|test]
│           ├── intrinsics/*txt
│           ├── pose/*txt
│           └── rgb/*jpg
│
├── 360_v2             # Link: https://jonbarron.info/mipnerf360/
│   └── [bicycle|bonsai|counter|garden|kitchen|room|stump]
│       ├── poses_bounds.npy
│       └── [images_2|images_4]
│
├── nerf_llff_data     # Link: https://drive.google.com/drive/folders/14boI-o5hGO9srnWaaogTU5_ji7wkX2S7
│   └── [fern|flower|fortress|horns|leaves|orchids|room|trex]
│       ├── poses_bounds.npy
│       └── [images_2|images_4]
│
└── co3d               # Link: https://github.com/facebookresearch/co3d
    └── [donut|teddybear|umbrella|...]
        ├── frame_annotations.jgz
        ├── set_lists.json
        └── [129_14950_29917|189_20376_35616|...]
            ├── images
            │   └── frame*.jpg
            └── masks
                └── frame*.png
</details>

GO

Custom casually captured scenes

Coming soon hopefully.

Development and tuning guide

Extention to new dataset

Adjusting the data related config fields to fit your camera coordinate system is recommend before implementing a new one. We provide two visualization tools for debugging.

  1. Inspect the camera and the allocated BBox.
    • Export via --export_bbox_and_cams_only {filename}.npz: 
      python run.py --config configs/nerf/mic.py --export_bbox_and_cams_only cam_mic.npz
    • Visualize the result: 
      python tools/vis_train.py cam_mic.npz
  2. Inspect the learned geometry after coarse optimization.
    • Export via --export_coarse_only {filename}.npz (assumed coarse_last.tar available in the train log): 
      python run.py --config configs/nerf/mic.py --export_coarse_only coarse_mic.npz
    • Visualize the result: 
      python tools/vis_volume.py coarse_mic.npz 0.001 --cam cam_mic.npz
Inspecting the cameras & BBoxInspecting the learned coarse volume

Speed and quality tradeoff

We have reported some ablation experiments in our paper supplementary material. Setting N_iters, N_rand, num_voxels, rgbnet_depth, rgbnet_width to larger values or setting stepsize to smaller values typically leads to better quality but need more computation. The weight_distortion affects the training speed and quality as well. Only stepsize is tunable in testing phase, while all the other fields should remain the same as training.

Advanced data structure

You will need them for scaling to a higher grid resolution. But we believe our simplest dense grid could still be your good starting point if you have other challenging problems to deal with.

Acknowledgement

The code base is origined from an awesome nerf-pytorch implementation, but it becomes very different from the code base now.