Awesome
<div align="center"> <h1>ML-SemReg</h1> <h3>ML-SemReg: Boosting Point Cloud Registration with Multi-level Semantic Consistency</h3>Shaocheng Yan<sup>1</sup>, Pengcheng Shi<sup>2</sup>, Jiayuan Li<sup>1 :email:</sup>
<sup>1</sup>School of Remote Sensing and Information Engineering, Wuhan University, <sup>2</sup>School of Computer Science, Wuhan University
(*) equal contribution, (<sup>:email:</sup>) corresponding author.
ML-SemReg is a new plug-and-play method for boosting point cloud registration, utilizing multi-level semantic consistency. Its core idea is to address inter- and intra-class mismatchings (outliers) ultilizing multi-level semantic consistency.
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Demo
Installation
conda create -n mlsemreg python=3.9
conda activate mlsemreg
pip install -r requirements.txt
# please check your CUDA version
pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117
Run demo.py
# a demo using KITTI medium dataset
python -m demo
python -m demo -is_vis
Citation
If you use this codebase, or otherwise find our work valuable, please cite ML-SemReg:
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