GO-Surf: Neural Feature Grid Optimization for Fast, High-Fidelity RGB-D Surface Reconstruction

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GO-Surf: Neural Feature Grid Optimization for Fast, High-Fidelity RGB-D Surface Reconstruction
Jingwen Wang, Tymoteusz Bleja, Lourdes Agapito
3DV 2022 (Oral)

This repository contains the code for GO-Surf, a direct feature grid optimization method for accurate and fast surface reconstruction from RGB-D sequences.

Updates

• 📣 Main training code and data [2022-09-11].
• 📣 Extracting colored mesh [2022-11-13].
• 📣 Sequential Mapping.

Method Overview

GO-Surf uses multi-level feature grids and two shallow MLP decoders. Given a sample point along a ray, each grid is queried via tri-linear interpolation. Multi-level features are concatenated and decoded into SDF, and used to compute the sample weight. Color is decoded separately from the finest grid. Loss terms are applied to SDF values, and rendered depth and color. The gradient of the SDF is calculated at each query point and used for Eikonal and smoothness regularization.

Quick Start

1. Installation

Clone GO-Surf repo

git clone https://github.com/JingwenWang95/go-surf.git
cd go-surf

Create environment

The code is tested with Python 3.9 and PyTorch 1.11 with CUDA 11.3. GO-Surf requires smooth_sampler, which is a drop-in replacement for PyTorch's grid sampler that support double back-propagation. Also the following packages are required:

You can create an anaconda environment with those requirements by running:

conda env create -f environment.yml
conda activate go_surf

Compile C++ extensions

Then install the external Marching Cubes dependency in the same environment:

# compile marching cubes
cd external/NumpyMarchingCubes
python setup.py install

2. Dataset

Synthetic Dataset

We use the synthetic dataset from NeuralRGB-D which contains 10 synthetic sequences with GT meshes and camera poses. You can download it from here (8.8 GB). You can also find the original link in NeuralRGB-D's Github repo from here.

The data of each scene is organised as follows:

<scene_name>            # args.scene in command line args
├── depth               # raw (real data) or ground truth (synthetic data) depth images (optional)
    ├── depth0.png     
    ├── depth1.png
    ├── depth2.png
    ...
├── depth_filtered      # filtered depth images
    ├── depth0.png     
    ├── depth1.png
    ├── depth2.png
    ...
├── depth_with_noise    # depth images with synthetic noise and artifacts (optional)
    ├── depth0.png     
    ├── depth1.png
    ├── depth2.png
    ...
├── images              # RGB images
    ├── img0.png     
    ├── img1.png
    ├── img2.png
    ...
├── focal.txt           # focal length
├── poses.txt           # ground truth poses (optional)
├── trainval_poses.txt  # camera poses used for optimization
├── gt_mesh.ply         # ground-truth mesh
├── gt_mesh_culled.ply  # culled ground-truth mesh for evaluation

Note that poses.txt contains gt poses defined in the same world coordinate as gt mesh, while trainval_poses.txt contains initial camera poses estimated by BundleFusion which are aligned with the first camera pose. In our dataloader we pre-align all the BundleFusion poses to the world coordinate for simplicity. Also note that both poses follow the OpenGL convention (right-upward-backward).

ScanNet Dataset

We also tested our code on real-world sequences from ScanNet. You can download the sequences following the instructions on their website.

Don't forget to change the datasets_dir in the config files to the dataset root directory after downloading the datasets!

3. Run

Training

You can start training by running:

python train.py --scene grey_white_room  --exp_name test

Note that scene must correspond to the config files defined under configs/. For the list of scenes you can refer to here. After training, the log files and checkpoints will be saved under logs/${scene}/${exp_name}

Mesh Extraction

To extract the mesh of trained scene, simply run:

python reconstruct.py --scene grey_white_room  --exp_name test

The extracted mesh will be saved under the directorylogs/${scene}/${exp_name}/mesh/. If you want colored mesh, simply add the option --color_mesh:

python reconstruct.py --scene scene0000_00  --exp_name test_color --color_mesh

You may want to switch off the use_view_dirs in color decoder if you want a good-looking colored mesh.

Evaluation

For evaluation, run:

python eval_mesh.py --scene grey_white_room  --exp_name test --n_iters 10000 --remove_missing_depth

which will first re-align and cull the original mesh, and then do the evaluation. Intermediate meshes and evaluation results are saved under logs/${scene}/${exp_name}/mesh/

Citation

If you use this code in your research, please consider citing:

@inproceedings{wang2022go-surf,
  author={Wang, Jingwen and Bleja, Tymoteusz and Agapito, Lourdes},
  booktitle={2022 International Conference on 3D Vision (3DV)},
  title={GO-Surf: Neural Feature Grid Optimization for Fast, High-Fidelity RGB-D Surface
  Reconstruction},
  year={2022},
  organization={IEEE}
}

Acknowledgement

This work was supported from a sponsored research award by Cisco Research and by the UCL Centre for Doctoral Training in Foundational AI under UKRI grant number EP/S021566/1.

Some code snippets are borrowed from neurecon and NeRF-pytorch. The Marching Cubes implementation was from NeuralRGB-D. Special thanks to Dejan Azinović for providing additional details on culling and evaluation scripts!