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:t-rex:[ECCV‘22] Pytorch implementation of 'Object-Compositional Neural Implicit Surfaces'
Object-Compositional Neural Implicit Surfaces
Project Page | Paper

This repository contains an implementation for the paper:
Object-Compositional Neural Implicit Surfaces.
Qianyi Wu, Xian Liu, Yuedong Chen, Kejie Li, Chuanxia Zheng, Jianfei Cai, Jianmin Zheng.
The paper introduce ObjectSDF: a volume rendering framework for object-compositional implicit neural surfaces, allowing to learn high fidelity geometry of each object from a sparse set of input images and corresponding semantic segmentation map.
Setup
Installation Requirmenets
The code is compatible with python 3.9 and pytorch 1.10.1. In addition, the following packages are required: numpy, pyhocon, plotly, scikit-image, trimesh, imageio, opencv, torchvision.
You can create an anaconda environment called objsdf with the required dependencies by running:
conda env create -f environment.yml
conda activate objsdf
Data
We provide the installation guidence to use our code in Toydesk dataset. At first, you need to download Toydesk dataset and put it in the './data' folder. Then
cd data
bash process_toydesk.sh
We require the RGB images with the corresponding semantic segmentation map for model training. After running the above script, you will get the corresponding file for two desk scenes.
Usage
For example, if you would like to train ObjectSDF on Toydesk 2, please run:
cd ./code
python training/exp_runner.py --conf confs/toydesk2.conf --train_type objsdf
Citation
If you use this project for your research, please cite our paper.
@article{wu2022objectsdf,
title={Object-Compositional Neural Implicit Surfaces},
author={Wu, Qianyi and Liu, Xian and Chen, Yuedong and Li, Kejie and Zheng, Chuanxia and Cai, Jianfei and Zheng, Jianmin},
journal={ECCV},
year={2022}
}
Related Links
If you are interested in NeRF / neural implicit representions + semantic map, we would also like to recommend you to check out our other related works:
Acknowledgments
Our implementation was mainly inspired by VolSDF.