Awesome
HASSC
Song Wang, Jiawei Yu, Wentong Li, Wenyu Liu, Xiaolu Liu, Junbo Chen*, Jianke Zhu*
This is the official implementation of Not All Voxels Are Equal: Hardness-Aware Semantic Scene Completion with Self-Distillation (CVPR 2024) [Paper] [Video].
<p align="center"> <a><img src="fig/framework.png" width="90%"></a> </p>Preparation
SemanticKITTI Download
- The semantic scene completion dataset v1.1 (SemanticKITTI voxel data, 700 MB) from SemanticKITTI website.
- The RGB images (Download odometry data set (color, 65 GB)) from KITTI Odometry website.
Environment Setup
We release the HASSC implementation with VoxFormer-T, please refer the environment setup in the original repo.
Run and Eval
Train the SSC model with our proposed HASSC on 4 GPUs
./tools/dist_train.sh ./projects/configs/hassc/hassc-voxformer-T.py 4
Eval the SSC model with our proposed HASSC on 4 GPUs
./tools/dist_test.sh ./projects/configs/hassc/hassc-voxformer-T.py ./path/to/ckpts.pth 4
Acknowledgement
Many thanks to these excellent open source projects: VoxFormer, mmdetction3d, PointRend
Citations
@inproceedings{wang2024not,
title={Not All Voxels Are Equal: Hardness-Aware Semantic Scene Completion with Self-Distillation},
author={Wang, Song and Yu, Jiawei and Li, Wentong and Liu, Wenyu and Liu, Xiaolu and Chen, Junbo and Zhu, Jianke},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2024}
}