Point-NeRF: Point-based Neural Radiance Fields (CVPR 2022 Oral 🤩)

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Project Sites | Paper | Primary contact: Qiangeng Xu

Point-NeRF uses neural 3D point clouds, with associated neural features, to model a radiance field. Point-NeRF can be rendered efficiently by aggregating neural point features near scene surfaces, in a ray marching-based rendering pipeline. Moreover, Point-NeRF can be initialized via direct inference of a pre-trained deep network to produce a neural point cloud; this point cloud can be finetuned to surpass the visual quality of NeRF with 30X faster training time. Point-NeRF can be combined with other 3D reconstruction methods and handles the errors and outliers in such methods via a novel pruning and growing mechanism.

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Reference

Please cite our paper if you are interested
<strong>Point-NeRF: Point-based Neural Radiance Fields</strong>.    

@inproceedings{xu2022point,
  title={Point-nerf: Point-based neural radiance fields},
  author={Xu, Qiangeng and Xu, Zexiang and Philip, Julien and Bi, Sai and Shu, Zhixin and Sunkavalli, Kalyan and Neumann, Ulrich},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={5438--5448},
  year={2022}
}

Updates

1. To replace pycuda, we have implemented the pytorch cuda functions when using world coordinates to group neural points. Simply set wcoord_query=-1 in your configuration file if the original setting is wcoord_query=1 (see dev_scripts/w_n360/chair_cuda.sh).
2. We have received constructive feedbacks that when Point-NeRF use MVSNet to reconstruct point cloud, the point fusion after depth estimation by MVSNet will use the alpha channel information in the NeRF-Synthetic Dataset. It is due to the fact that MVSNet cannot handle background very well. To improve the fairness, we include new training scripts and results of PointNeRF + MVSNet when using background color for filtering. The results (see below) are similar to the ones that are previously reported.

| | Chair | Drums | Lego | Mic | Materials | Ship | Hotdog | Ficus | Avg | | ---- | ---- | ---- | --- | ---- | ---- | ---- | ------- | ------- |------- | | PSNR | 35.60 | 26.04 | 35.27 | 35.91 | 29.65 | 30.61 | 37.34 | 35.61 | 33.25 | | SSIM | 0.991 | 0.954 | 0.989 | 0.994 | 0.971 | 0.938 | 0.991 | 0.992 | 0.978 | | LPIPSVgg | 0.023 | 0.078 | 0.021 | 0.014 | 0.071 | 0.129 | 0.036 | 0.025 | 0.050 | | LPIPSAlex | 0.010 | 0.055 | 0.010 | 0.007 | 0.041 | 0.076 | 0.016 | 0.011 | 0.028 |

This issue only affacts situations when Point-NeRF uses MVSNet on NeRF-Synthetic Dataset. The Colmap results and results on other datasets are not impacted.
An even more reasonable reconstruction approach should exclude using the knowledge of background color or other point filtering. Therefore, we suggest users to combine PointNeRF with more powerful MVS models, such as TransMVS.

Overal Instruction

1. Please first install the libraries as below and download/prepare the datasets as instructed.
2. Point Initialization: Download pre-trained MVSNet as below and train the feature extraction from scratch or directly download the pre-trained models. (Obtain 'MVSNet' and 'init' folder in checkpoints folder)
3. Per-scene Optimization: Download pre-trained models or optimize from scratch as instructed.

For nerfsynthetic, colmap_nerfsynthetic, tanks&temples, scannet and dtu,
We provide all the checkpoint_files google drive | baidu wangpan; all the images and scores of the test results google drive | baidu wangpan; and video results google drive | baidu wangpan;

We also share the visual results of npbg, nsvf and ibrnet on the Nerf Synthetic dataset generated by our machine google drive; Please cite their papers accordingly if interested.

Installation

Requirements

All the codes are tested in the following environment:

• Linux (tested on Ubuntu 16.04, 18.04, 20.04)
• Python 3.6+
• PyTorch 1.7 or higher (tested on PyTorch 1.7, 1.8.1, 1.9, 1.10)
• CUDA 10.2 or higher

Install

Install the dependent libraries as follows:

• Install the dependent python libraries:

pip install torch==1.8.1+cu102 h5py
pip install imageio scikit-image

• Install pycuda (crucial) following: https://documen.tician.de/pycuda/install.html
• Install torch_scatter following: https://github.com/rusty1s/pytorch_scatter

We develope our code with pytorch1.8.1, pycuda2021.1, and torch_scatter 2.0.8

Data Preparation

The layout should looks like this, we provide all data folder here: google_drive, except for scannet (We take it down on Mar 8th 2023, to respect Scannet's policy, please go to Scannet's official website for data.)

pointnerf
├── data_src
│   ├── dtu
    │   │   │──Cameras
    │   │   │──Depths
    │   │   │──Depths_raw
    │   │   │──Rectified
    ├── nerf
    │   │   │──nerf_synthetic
    │   │   │──nerf_synthetic_colmap
    ├── TanksAndTemple
    ├── scannet
    │   │   │──scans 
    |   │   │   │──scene0101_04
    |   │   │   │──scene0241_01

Or you can download using the official links as follows:

DTU:

Download the preprocessed DTU training data and Depth_raw from original MVSNet repo and unzip.

NeRF Synthetic

Download nerf_synthetic.zip from here under ``data_src/nerf/''

Tanks & Temples

Follow Neural Sparse Voxel Fields and download Tanks&Temples | download (.zip) | 0_* (training) 1_* (testing) as: ``data_src/TanksAndTemple/''

ScanNet

Download and extract ScanNet by following the instructions provided at http://www.scan-net.org/. The detailed steps including:

• Go to http://www.scan-net.org and fill & sent the request form.
• You will get a email that has command instruction and a download-scannet.py file, this file is for python 2, you can use our download-scannet.py in the ``data'' directory for python 3.
• clone the official repo:

  git clone https://github.com/ScanNet/ScanNet.git
  

• Download specific scenes (used by NSVF):

   python data/download-scannet.py -o ../data_src/scannet/ id scene0101_04 
   python data/download-scannet.py -o ../data_src/scannet/ id scene0241_01
  

• Process the sens files:

    python ScanNet/SensReader/python/reader.py --filename data_src/nrData/scannet/scans/scene0101_04/scene0101_04.sens  --output_path data_src/nrData/scannet/scans/scene0101_04/exported/ --export_depth_images --export_color_images --export_poses --export_intrinsics
    
    python ScanNet/SensReader/python/reader.py --filename data_src/nrData/scannet/scans/scene0241_01/scene0241_01.sens  --output_path data_src/nrData/scannet/scans/scene0241_01/exported/ --export_depth_images --export_color_images --export_poses --export_intrinsics
  

Point Initialization / Generalization:

  Download pre-trained MVSNet checkpoints:

We trained MVSNet on DTU. You can Download ''MVSNet'' directory from google drive and place them under '''checkpoints/'''

  Train 2D feature extraction and point representation

  Directly use our trained checkpoints files:

Download ''init'' directory from google drive. and place them under '''checkpoints/'''

  Or train from scratch:

Train for point features of 63 channels (as in paper)

bash dev_scripts/ete/dtu_dgt_d012_img0123_conf_color_dir_agg2.sh

Train for point features of 32 channels (better for per-scene optimization)

bash dev_scripts/ete/dtu_dgt_d012_img0123_conf_agg2_32_dirclr20.sh

After the training, you should pick a checkpoint and rename it to best checkpoint, e.g.:

cp checkpoints/dtu_dgt_d012_img0123_conf_color_dir_agg2/250000_net_ray_marching.pth  checkpoints/dtu_dgt_d012_img0123_conf_color_dir_agg2/best_net_ray_marching.pth

cp checkpoints/dtu_dgt_d012_img0123_conf_color_dir_agg2/250000_net_mvs.pth  checkpoints/dtu_dgt_d012_img0123_conf_color_dir_agg2/best_net_mvs.pth

  Test feed forward inference on dtu scenes

These scenes that are selected by MVSNeRF, please also refer their code to understand the metrics calculation.

bash dev_scripts/dtu_test_inf/inftest_scan1.sh
bash dev_scripts/dtu_test_inf/inftest_scan8.sh
bash dev_scripts/dtu_test