Summary of Retr: Multi-view Radar Detection Transformer For Indoor Perception, by Ryoma Yataka et al.
RETR: Multi-View Radar Detection Transformer for Indoor Perception
by Ryoma Yataka, Adriano Cardace, Pu Perry Wang, Petros Boufounos, Ryuhei Takahashi
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Differential Geometry (math.DG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 Radar dEtection TRansformer (RETR) is an extension of the popular DETR architecture, specifically designed for multi-view radar perception. RETR inherits the benefits of DETR, eliminating the need for hand-crafted components for object detection and segmentation in the image plane. The approach incorporates carefully designed modifications, including depth-prioritized feature similarity via a tunable positional encoding (TPE), tri-plane loss from both radar and camera coordinates, and learnable radar-to-camera transformation via reparameterization, to account for the unique multi-view radar setting. Evaluated on two indoor radar perception datasets, the proposed approach outperforms existing state-of-the-art methods by a margin of 15.38+ AP for object detection and 11.91+ IoU for instance segmentation, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to detect objects using radar waves indoors. It’s like a special kind of camera that uses sound waves instead of light. The new approach is called RETR and it works by combining different types of data from the radar waves and cameras. This helps the system understand what’s happening in each room better. The results show that this new method is much better than existing methods at detecting objects and understanding where they are. |
Keywords
» Artificial intelligence » Instance segmentation » Object detection » Positional encoding » Transformer
