Summary of Lerojd: Lidar Extended Radar-only Object Detection, by Patrick Palmer et al.
LEROjD: Lidar Extended Radar-Only Object Detection
by Patrick Palmer, Martin Krüger, Stefan Schütte, Richard Altendorfer, Ganesh Adam, Torsten Bertram
First submitted to arxiv on: 9 Sep 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 The paper presents a study on accurate 3D object detection using imaging radar sensors, which are cost-effective and robust alternatives to lidar sensors. The authors investigate two strategies for transferring knowledge from lidar to radar-only object detectors: multi-stage training with sequential thin-out of lidar point clouds and cross-modal knowledge distillation. They compare the performance of these approaches on different object detectors and demonstrate significant gains in mean Average Precision, up to 4.2 percentage points with multi-stage training and up to 3.9 percentage points with knowledge distillation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding ways to make computer vision work better using special sensors called imaging radar. These sensors are good at detecting objects in the world, but they don’t have as much detail as other sensors called lidar. The authors try two different methods to help these sensors do a better job: one way is to gradually remove some of the information from the lidar sensor, and another way is to teach the radar sensor how to detect objects by showing it examples from the lidar sensor. They test these methods on different computer vision models and find that they can make them work better. |
Keywords
» Artificial intelligence » Knowledge distillation » Mean average precision » Object detection
