Summary of What Matters in Range View 3d Object Detection, by Benjamin Wilson et al.
What Matters in Range View 3D Object Detection
by Benjamin Wilson, Nicholas Autio Mitchell, Jhony Kaesemodel Pontes, James Hays
First submitted to arxiv on: 23 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 proposes a new range-view 3D object detection model that achieves state-of-the-art performance without using multiple techniques proposed in past literature. The model is based on the range-view representation, which losslessly encodes the entire lidar sensor output. Experiments are conducted on two modern datasets, Argoverse 2 and Waymo Open, revealing key insights into input feature dimensionality’s impact on overall performance, the effectiveness of classification losses grounded in 3D spatial proximity, and the benefits of addressing non-uniform lidar density through straightforward range subsampling. The model improves AP by 2.2% on the Waymo Open dataset while maintaining a runtime of 10 Hz. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new method for detecting objects from lidar data. It uses a special kind of computer vision called “range-view” to analyze the data. This approach is different from others that also try to detect objects from lidar data. The researchers tested their method on two large datasets and found that it worked really well. They even improved upon previous results by 2.2%! The method is fast, too – it can process data in just 10 seconds. |
Keywords
» Artificial intelligence » Classification » Object detection
