• TensoRF
• • Project page | Paper
  • Installation
  • • • Tested on Ubuntu 20.04 + Pytorch 1.10.1
  • Dataset
  • Quick Start
  • • For pretrained checkpoints and results please see:
  • Rendering
  • Extracting mesh
  • Training with your own data
  • Citation

[注意] 当前代码基于“msadapter”迁移，未适配更名后的“mindtorch“包。所以在使用时，需要参考MindTorch主页安装历史配套的”msadapter“版本。

pip install configargparse kornia

TensoRF

Project page | Paper

This repository contains a pytorch implementation for the paper: TensoRF: Tensorial Radiance Fields. Our work present a novel approach to model and reconstruct radiance fields, which achieves super
fast training process, compact memory footprint and state-of-the-art rendering quality.

https://user-images.githubusercontent.com/16453770/158920837-3fafaa17-6ed9-4414-a0b1-a80dc9e10301.mp4

Installation

Tested on Ubuntu 20.04 + Pytorch 1.10.1

Install environment:

conda create -n TensoRF python=3.8
conda activate TensoRF
pip install torch torchvision
pip install tqdm scikit-image opencv-python configargparse lpips imageio-ffmpeg kornia lpips tensorboard

Dataset

• Synthetic-NeRF
• Synthetic-NSVF
• Tanks&Temples
• Forward-facing

Quick Start

The training script is in train.py, to train a TensoRF:

python train.py --config configs/lego.txt

we provide a few examples in the configuration folder, please note:

dataset_name, choices = ['blender', 'llff', 'nsvf', 'tankstemple'];

shadingMode, choices = ['MLP_Fea', 'SH'];

model_name, choices = ['TensorVMSplit', 'TensorCP'], corresponding to the VM and CP decomposition.
You need to uncomment the last a few rows of the configuration file if you want to training with the TensorCP model；

n_lamb_sigma and n_lamb_sh are string type refer to the basis number of density and appearance along XYZ
dimension;

N_voxel_init and N_voxel_final control the resolution of matrix and vector;

N_vis and vis_every control the visualization during training;

You need to set --render_test 1/--render_path 1 if you want to render testing views or path after training.

More options refer to the opt.py.

For pretrained checkpoints and results please see:

https://1drv.ms/u/s!Ard0t_p4QWIMgQ2qSEAs7MUk8hVw?e=dc6hBm

Rendering

python train.py --config configs/lego.txt --ckpt path/to/your/checkpoint --render_only 1 --render_test 1 

You can just simply pass --render_only 1 and --ckpt path/to/your/checkpoint to render images from a pre-trained
checkpoint. You may also need to specify what you want to render, like --render_test 1, --render_train 1 or --render_path 1.
The rendering results are located in your checkpoint folder.

Extracting mesh

You can also export the mesh by passing --export_mesh 1:

python train.py --config configs/lego.txt --ckpt path/to/your/checkpoint --export_mesh 1

Note: Please re-train the model and don't use the pretrained checkpoints provided by us for mesh extraction,
because some render parameters has changed.

Training with your own data

We provide two options for training on your own image set:

1. Following the instructions in the NSVF repo, then set the dataset_name to 'tankstemple'.
2. Calibrating images with the script from NGP:
   python dataLoader/colmap2nerf.py --colmap_matcher exhaustive --run_colmap, then adjust the datadir in configs/your_own_data.txt. Please check the scene_bbox and near_far if you get abnormal results.

Citation

If you find our code or paper helps, please consider citing:

@INPROCEEDINGS{Chen2022ECCV,
  author = {Anpei Chen and Zexiang Xu and Andreas Geiger and Jingyi Yu and Hao Su},
  title = {TensoRF: Tensorial Radiance Fields},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year = {2022}
}