Summary of A Survey on Occupancy Perception For Autonomous Driving: the Information Fusion Perspective, by Huaiyuan Xu et al.
A Survey on Occupancy Perception for Autonomous Driving: The Information Fusion Perspective
by Huaiyuan Xu, Junliang Chen, Shiyu Meng, Yi Wang, Lap-Pui Chau
First submitted to arxiv on: 8 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 This survey paper reviews recent advancements in 3D occupancy perception technology, a crucial component of autonomous driving systems. The researchers summarize general network pipelines, highlight information fusion techniques, and discuss effective training methods for various input modalities. They evaluate the performance of state-of-the-art models on popular datasets, identify challenges, and propose future research directions. The paper aims to inspire the community and encourage further research in this field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous vehicles need to understand their surroundings to navigate safely. One way they do this is by using 3D occupancy perception technology. This technology helps them see and understand complex environments like cities or forests. It’s like having a superpower that lets them perceive the world in three dimensions! The researchers in this paper looked at how people have used different methods to make these technologies work, what kinds of data they’ve used, and which ones are the most accurate. |
