Summary of Spotnet: An Image Centric, Lidar Anchored Approach to Long Range Perception, by Louis Foucard et al.
SpotNet: An Image Centric, Lidar Anchored Approach To Long Range Perception
by Louis Foucard, Samar Khanna, Yi Shi, Chi-Kuei Liu, Quinn Z Shen, Thuyen Ngo, Zi-Xiang Xia
First submitted to arxiv on: 24 May 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 This paper proposes SpotNet, a novel approach for long-range 3D object detection using LiDAR and image sensors. The model learns to detect objects in both 2D and 3D spaces simultaneously, leveraging the strengths of each sensor. Unlike previous methods that scale with range as O(r^2), SpotNet scales as O(1) with range, making it more efficient for distant object detection. The authors demonstrate accurate 3D object detection using very sparse LiDAR support and show that anchoring detections on LiDAR points removes the need for distance regression, allowing the model to transfer between different image resolutions without retraining. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to find objects in 3D space using cameras and lasers. The idea is to use both sensors together to learn what things look like from different angles. This helps with detecting objects that are far away. The system works well even when there isn’t much laser data available. One advantage of this approach is that it can work on images of different sizes without needing to relearn. |
Keywords
» Artificial intelligence » Object detection » Regression
