The repository contains the official implementation of source code and pre-trained models of our paper:"Urban Radiance Field Representation with Deformable Neural Mesh Primitives". It is a new representation to model urban scenes for efficient and high-quality rendering!
- 2023.07.21: The ⭐⭐⭐The source code is released!⭐⭐⭐ Try it!
- 2023.07.21: The 🔥🔥🔥The pre-print is released!🔥🔥🔥 Refer to it for more details!
- 2023.07.19: The project page is created. Check it out for an overview of our work!
We conduct experiments on two outdoor datasets: KITTI-360 dataset, Waymo-Open-Dataset. Please refer to preprocess/README.md for more details.
- Compile fairnr.
python setup.py build_ext --inplace
- Main requirements:
- CUDA (tested on cuda-11.1)
- PyTorch (tested on torch-1.9.1)
- pytorch3d
- torchsparse
- Other requirements are provided in
requirements.txt
-
Optimize geometry using our pre-trained auto-encoder by running
sh scripts/train_${DATASET}_geo.sh
. (Please specifySEQUENCE
,DATA_ROOT
,LOG_DIR
andCKPT_DIR
in the script.) -
Train radiance field by running
sh scripts/train_${DATASET}_render.sh
. (Please specifySEQUENCE
,DATA_ROOT
,LOG_DIR
,CKPT_DIR
andPRETRAINED_GEO
in the script.)
You can run scripts/test_kitti360.sh
for evaluation. (Please specify SAVE_DIR
, DATA_ROOT
and the pretrained files in the script.)
- Release Code and pretrained model
- Technical Report
- Project page
If you find this project useful for your work, please consider citing:
@article{lu2023dnmp,
author = {Lu, Fan and Xu, Yan and Chen, Guang and Li, Hongsheng and Lin, Kwan-Yee and Jiang, Changjun},
title = {Urban Radiance Field Representation with Deformable Neural Mesh Primitives},
journal = {ICCV},
year = {2023},
}