Summary of Seabird: Segmentation in Bird’s View with Dice Loss Improves Monocular 3d Detection Of Large Objects, by Abhinav Kumar et al.
SeaBird: Segmentation in Bird’s View with Dice Loss Improves Monocular 3D Detection of Large Objects
by Abhinav Kumar, Yuliang Guo, Xinyu Huang, Liu Ren, Xiaoming Liu
First submitted to arxiv on: 29 Mar 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 The paper highlights a critical issue with monocular 3D detectors, which excel at detecting small objects like cars but struggle to generalize to larger objects. This limitation is attributed to the sensitivity of depth regression losses to noise in large object representations. The authors investigate and compare different loss functions, demonstrating that the dice loss is more robust to noise and leads to better model convergence for large objects. Building on this insight, they propose SeaBird, a novel 3D detection method that integrates BEV segmentation with foreground object detection, achieving state-of-the-art results on the KITTI-360 leaderboard and improving existing detectors on the nuScenes leaderboard. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about a problem with machines that can see in 3D. These machines are really good at finding small things like cars, but they have trouble finding bigger things. The authors think this is because the way they calculate distances from images is sensitive to mistakes when looking at big objects. They study different ways of calculating these distances and find that one method called “dice loss” does a better job than others at handling mistakes. Based on this, they created a new way for machines to see in 3D that works really well on big objects. |
Keywords
» Artificial intelligence » Object detection » Regression
