This is the source code of "TriVol: Point Cloud Rendering via Triple Volumes" (CVPR-2023).
- Linux
- Python == 3.8
- Pytorch == 1.13.0
- CUDA == 11.7
You can directly install the requirements through:
$ conda env create -f environment.yml-
Create Environment
$ conda create --name trivol python=3.8 $ conda activate trivol
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Pytorch (Please first check your cuda version)
$ conda install pytorch==1.13.0 torchvision==0.14.0 torchaudio==0.13.0 pytorch-cuda=11.7 -c pytorch -c nvidia
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$ pip install git+https://github.com/KAIR-BAIR/nerfacc.git
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pip install torch-scatter -f https://data.pyg.org/whl/torch-1.13.0+cu117.html
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Other python packages: open3d, opencv-python, etc.
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Dataset: Download our processed ShapeNet Car from HuggingFace.
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Run: we recommand you run the code in multi-gpu mode.
$ conda activate trivol $ python train.py \ --scene_dir /path/to/shapenet \ --dataset shapenet \ --val_mode val \ --max_epochs 200 \ --lr 0.0005 \ --batch_size 1 \ --ngpus 4 \ --feat_dim 16 \ --patch_size 64 \ --exp_name shapenet_car -
example results
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Dataset: Download and extract data from original ScanNet-V2 preprocess.
Dataset structure:
── scannet └── scene0000_00 ├── pose └──1.txt ├── intrinsic └──*.txt ├── color └──1.jpg └── scene0000_00_vh_clean_2.ply └── images.txt └── scene0000_01 -
Run: we recommand you run the code in multi-gpu mode.
$ conda activate trivol $ python train.py \ --scene_dir /path/to/scannet \ --dataset scannet \ --val_mode val \ --max_epochs 500 \ --lr 0.0005 \ --batch_size 1 \ --ngpus 4 \ --feat_dim 16 \ --patch_size 32 \ --exp_name scannet -
example results
Tao Hu - taohu@cse.cuhk.edu.hk
@InProceedings{Hu_2023_CVPR,
author = {Hu, Tao and Xu, Xiaogang and Chu, Ruihang and Jia, Jiaya},
title = {TriVol: Point Cloud Rendering via Triple Volumes},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2023},
pages = {20732-20741}
}