AeDet：方位不变的多视图3D目标检测
AeDet: Azimuth-invariant Multi-view 3D Object Detection
AI总结：
提出方位等变卷积AeConv和锚框以保留BEV径向对称性，并引入相机解耦虚拟深度统一深度预测，构建的AeDet在nuScenes上达62.0% NDS。
摘要：
近期基于LSS的多视图3D目标检测取得了巨大进展，通过卷积检测器处理鸟瞰图（BEV）中的特征。然而，典型的卷积忽略了BEV特征的径向对称性，并增加了检测器优化的难度。为了保留BEV特征的固有属性并简化优化，我们提出了方位等变卷积（AeConv）和方位等变锚框。AeConv的采样网格始终沿径向方向，因此能够学习方位不变的BEV特征。所提出的锚框使检测头能够学习预测与方位无关的目标。此外，我们引入了相机解耦的虚拟深度，以统一具有不同相机内参的图像的深度预测。由此产生的检测器被称为方位等变检测器（AeDet）。在nuScenes上进行了大量实验，AeDet实现了62.0%的NDS，大幅超越了PETRv2和BEVDepth等近期多视图3D目标检测器。项目页面：https://fcjian.github.io/aedet。
英文摘要：
Recent LSS-based multi-view 3D object detection has made tremendous progress, by processing the features in Brid-Eye-View (BEV) via the convolutional detector. However, the typical convolution ignores the radial symmetry of the BEV features and increases the difficulty of the detector optimization. To preserve the inherent property of the BEV features and ease the optimization, we propose an azimuth-equivariant convolution (AeConv) and an azimuth-equivariant anchor. The sampling grid of AeConv is always in the radial direction, thus it can learn azimuth-invariant BEV features. The proposed anchor enables the detection head to learn predicting azimuth-irrelevant targets. In addition, we introduce a camera-decoupled virtual depth to unify the depth prediction for the images with different camera intrinsic parameters. The resultant detector is dubbed Azimuth-equivariant Detector (AeDet). Extensive experiments are conducted on nuScenes, and AeDet achieves a 62.0% NDS, surpassing the recent multi-view 3D object detectors such as PETRv2 and BEVDepth by a large margin. Project page: https://fcjian.github.io/aedet.
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。
