Summary of Multi-space Alignments Towards Universal Lidar Segmentation, by Youquan Liu and Lingdong Kong and Xiaoyang Wu and Runnan Chen and Xin Li and Liang Pan and Ziwei Liu and Yuexin Ma
Multi-Space Alignments Towards Universal LiDAR Segmentation
by Youquan Liu, Lingdong Kong, Xiaoyang Wu, Runnan Chen, Xin Li, Liang Pan, Ziwei Liu, Yuexin Ma
First submitted to arxiv on: 2 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 M3Net is a novel framework for LiDAR segmentation that can handle multi-task, multi-dataset, and multi-modality tasks using a single set of parameters. The model combines large-scale driving datasets from different sensors and scenes, aligning them in data, feature, and label spaces during training. This approach allows M3Net to train state-of-the-art LiDAR segmentation models even with heterogeneous data. The framework is evaluated on twelve LiDAR segmentation datasets, achieving high mIoU scores on official benchmarks like SemanticKITTI (75.1%), nuScenes (83.1%), and Waymo Open (72.4%). M3Net’s robustness and generalizability make it a promising tool for safe autonomous driving perception. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary M3Net is a special computer program that helps cars see better in the dark or in bad weather. It can handle different types of data from various sensors and scenes, which makes it very good at recognizing objects like roads, buildings, and vehicles. The program trains itself using large amounts of data and then uses what it learned to make accurate predictions about what’s around the car. M3Net was tested on many different datasets and performed well, achieving high scores on official tests. |
Keywords
» Artificial intelligence » Multi task
