Summary of Fsmdet: Vision-guided Feature Diffusion For Fully Sparse 3d Detector, by Tianran Liu et al.
FSMDet: Vision-guided feature diffusion for fully sparse 3D detector
by Tianran Liu, Morteza Mousa Pasandi, Robert Laganiere
First submitted to arxiv on: 11 Sep 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 proposed FSMDet (Fully Sparse Multi-modal Detection) framework uses visual information to guide LiDAR feature diffusion while maintaining efficiency. It splits the vision-guided diffusion process into two modules: Shape Recover Layer (SRLayer) and Self Diffusion Layer (SDLayer). The SRLayer recovers object shape using RGB info, while the SDLayer spreads features to the center region using a visual prior. FSMDet improves LiDAR-only sparse models’ performance, reaching state-of-the-art (SOTA) in multimodal models, with up to 5x efficiency gain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FSMDet is a new way to detect objects in 3D space using both cameras and lasers. It’s really good at finding things and can do it much faster than before. The secret is breaking down the process into two steps: figuring out what an object looks like, and then spreading that information around. |
Keywords
» Artificial intelligence » Diffusion » Multi modal
