Summary of Multi-modal Data-efficient 3d Scene Understanding For Autonomous Driving, by Lingdong Kong and Xiang Xu and Jiawei Ren and Wenwei Zhang and Liang Pan and Kai Chen and Wei Tsang Ooi and Ziwei Liu
Multi-Modal Data-Efficient 3D Scene Understanding for Autonomous Driving
by Lingdong Kong, Xiang Xu, Jiawei Ren, Wenwei Zhang, Liang Pan, Kai Chen, Wei Tsang Ooi, Ziwei Liu
First submitted to arxiv on: 8 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this study, researchers aim to improve 3D scene understanding in autonomous driving by developing a semi-supervised learning framework for LiDAR semantic segmentation. They introduce LaserMix++, an advanced framework that combines laser beam manipulations from different LiDAR scans and incorporates camera-to-LiDAR correspondences to enhance data-efficient learning. The framework consists of three components: multi-modal LaserMix operation, camera-to-LiDAR feature distillation, and language-driven knowledge guidance using open-vocabulary models. Experimental results show that LaserMix++ outperforms fully supervised alternatives, achieving comparable accuracy with five times fewer annotations. This advancement highlights the potential of semi-supervised approaches in reducing reliance on labeled data for LiDAR-based 3D scene understanding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about making computers better at understanding what they see in 3D scenes, which is important for self-driving cars. The researchers are trying to figure out how to use cameras and lasers together to make the computer learn more efficiently without needing as much labeled data. They created a new way of doing this called LaserMix++, which combines different types of laser data and uses cameras to help the computer learn better. This new approach was tested on some popular datasets and showed that it can be just as good as using all the labeled data, but with less effort. |
Keywords
» Artificial intelligence » Distillation » Multi modal » Scene understanding » Semantic segmentation » Semi supervised » Supervised
