IPoD: Implicit Field Learning with Point Diffusion for Generalizable 3D Object Reconstruction from Single RGB-D Images
GAP Lab, Yushuang Wu
Paper - Project Website - Arxiv - Published in CVPR 2024 (Highlight).
If you find our code or paper useful for your project, please consider citing:
@inproceedings{wu2024ipod,
title={IPoD: Implicit Field Learning with Point Diffusion for Generalizable 3D Object Reconstruction from Single RGB-D Images},
author={Yushuang, Wu and Luyue, Shi and Junhao, Cai and Weihao, Yuan and Lingteng, Qiu and Zilong, Dong and Liefeng, Bo and Shuguang, Cui and Xiaoguang, Han},
booktitle={The IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR)},
year={2024},
}
This work was done by Yushuang Wu during intership at Alibaba Group supervised by Weihao Yuan. Official Code Link.
Please see INSTALL.md for information on installation.
Please see DATASET.md for information on data preparation.
To download the pretrained models (on CO3D-v2), run:
mkdir ckpts
cd ckpts
wget https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/YushuangWu/IPoD_ckpts/ipod_transformer_co3d.pth
To train from scratch, run:
sh train.sh
The arguements are used the same with ones in the repository of NU-MCC.
For evaluation/inference:
sh eval.sh
The argument --n_query_udf defines the total number of points in the final output. In general, the higher numbers result in more uniform point distribution and also longer inference time.
To run visualization, use --run_viz flag. The output will be generated to the folder specified in --exp_name. Visualization/evaluation from one class can be specified using --one_class [OBJECT_CLASS] flag. Point clouds can be exported by activating --save_pc flag.
On CO3Dv2 held-out categories:
On CO3Dv2 held-in categories:
On MVImgNet:
This codebase is mainly inherited from the repositories of NU-MCC and MCC.