Strivec: Sparse Tri-Vector Radiance Fields
Project Sites (coming soom) | Paper
Overal Instruction
- We build the initial geometry with the 1st stage of DVGO in our implementation by default, which is
use_geo = -1
in config files. - The geometry can be either initialized online (by default) or from other sources in
.txt
form, which can be enabled withuse_geo = 1
andpointfile = /your/file.txt
in config files. - You may ignore
preprosessing
folder, which is initially for our ealy trying and not used here. - You may refer to the comments in
./configs/synthetic-nerf/default/chair.txt
for the usage of hyperparameters.
For Synthetic-NeRF dataset, we provide the initial geometry from DVGO (which is the default one in our implementation) and from MVS. Feel free to try both (e.g., use_geo = 1
and pointfile = /your/mvs_file.txt
) to see the comparison.
For Scannet dataset, we use the initial geometry provided by the dataset itself. We convert the original .ply
file into .txt
and you may download from here.
Installation
Requirements
All the codes are tested in the following environment:
- Linux 18.04+
- Python 3.6+
- PyTorch 1.10+
- CUDA 10.2+
Data Preparation
And the layout should look like this:
Strivec
├── data
│ ├── nerf_synthetic
│ │ |──default
│ │ │ |──chair
│ │ │ │──drums
│ │ │ |──...
│ │ |──local_vm
│ │ │ |──chair
│ │ │ │──drums
│ │ │ |──...
├── scene0101_04 (scannet)
│ │ │──exported
│ │ │──scene0101_04_2d-instance-filt.zip
│ │ │──...
├── scene0241_01 (scannet)
│ │ │──exported
│ │ │──scene0241_01_2d-instance-filt.zip
│ │ │──...
├── TanksAndTemple
│ │ │──Barn
│ │ │──Caterpillar
│ │ │──...
├── 360 (Mip-NeRF360)
│ │ │──garden
│ │ │──room
│ │ │──...
Training & Evaluation
We not only provide the training and evaluation code to reproduce the results in the paper, but also the code of ablation that uses local VM tensors instead of local CP tensors (results are here).
# hierachical Strivec, without rotation (grid aligned)
python train_hier.py --config ./configs/synthetic-nerf/default/chair.txt
# local VM tensors instead of local CP tensors
train_dbasis.py --config ./configs/synthetic-nerf/local_vm/chair.txt
Visualization
We visualize the local tensors of different scales into ./log/your_scene/rot_tensoRF/0_lvl_k.ply
, where k is the kth scale.
Citation
If you find our code or paper helps, please consider citing:
@INPROCEEDINGS{gao2023iCCV,
author = {Quankai Gao and Qiangeng Xu and Hao su and Ulrich Neumann and Zexiang Xu},
title = {Strivec: Sparse Tri-Vector Radiance Fields},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
year = {2023}
}