RETR：面向室内感知的多视角雷达检测Transformer
RETR: Multi-View Radar Detection Transformer for Indoor Perception
AI总结：
针对室内多视角雷达感知，提出RETR模型，基于DETR架构，通过深度优先特征相似性、三平面损失和可学习变换，在检测和分割上分别超越SOTA 15.38+ AP和11.91+ IoU。
摘要：
室内雷达感知因新兴汽车成像雷达发展带来的低成本优势，以及减少隐私担忧和在危险条件（如火灾和烟雾）下的可靠性，而受到越来越多的关注。然而，现有的雷达感知流程未能考虑多视角雷达设置的独特特征。在本文中，我们提出了雷达检测Transformer（RETR），这是流行的DETR架构的扩展，专为多视角雷达感知而设计。RETR继承了DETR的优势，无需在图像平面上进行目标检测和分割的手工组件。更重要的是，RETR纳入了精心设计的修改，例如：1）通过可调位置编码（TPE）实现深度优先的特征相似性；2）来自雷达和相机坐标的三平面损失；3）通过重参数化实现可学习的雷达到相机变换，以适应独特的多视角雷达设置。在两个室内雷达感知数据集上的评估中，我们的方法在目标检测和实例分割方面分别以15.38+ AP和11.91+ IoU的差距超越了现有最先进的方法。我们的实现可在https://github.com/merlresearch/radar-detection-transformer获取。
英文摘要：
Indoor radar perception has seen rising interest due to affordable costs driven by emerging automotive imaging radar developments and the benefits of reduced privacy concerns and reliability under hazardous conditions (e.g., fire and smoke). However, existing radar perception pipelines fail to account for distinctive characteristics of the multi-view radar setting. In this paper, we propose Radar dEtection TRansformer (RETR), an extension of the popular DETR architecture, tailored for multi-view radar perception. RETR inherits the advantages of DETR, eliminating the need for hand-crafted components for object detection and segmentation in the image plane. More importantly, RETR incorporates carefully designed modifications such as 1) depth-prioritized feature similarity via a tunable positional encoding (TPE); 2) a tri-plane loss from both radar and camera coordinates; and 3) a learnable radar-to-camera transformation via reparameterization, to account for the unique multi-view radar setting. Evaluated on two indoor radar perception datasets, our 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. Our implementation is available at https://github.com/merlresearch/radar-detection-transformer.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
