Summary of Multicorrupt: a Multi-modal Robustness Dataset and Benchmark Of Lidar-camera Fusion For 3d Object Detection, by Till Beemelmanns et al.
MultiCorrupt: A Multi-Modal Robustness Dataset and Benchmark of LiDAR-Camera Fusion for 3D Object Detection
by Till Beemelmanns, Quan Zhang, Christian Geller, Lutz Eckstein
First submitted to arxiv on: 18 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 Multi-modal 3D object detection models have excelled on computer vision benchmarks like nuScenes, but their reliance on densely sampled LiDAR point clouds and meticulously calibrated sensor arrays poses challenges for real-world applications. To address this challenge, the authors introduce MultiCorrupt, a comprehensive benchmark designed to evaluate the robustness of multi-modal 3D object detectors against ten distinct types of corruptions. They evaluate five state-of-the-art multi-modal detectors on MultiCorrupt and analyze their performance in terms of resistance ability. The results show that existing methods exhibit varying degrees of robustness depending on the type of corruption and their fusion strategy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multi-modal 3D object detection models are super cool, but they have a problem. They need special sensors to work well, which can be tricky in real-life situations. The authors made a special test called MultiCorrupt to see how good these models are at handling problems like bad weather or sensor glitches. They tested five of the best models and found out that some are better than others at handling certain kinds of problems. |
Keywords
» Artificial intelligence » Multi modal » Object detection
