KPDet: Keypoint-based 3D object detection with Parametric Radius Learning

Abstract

3D object detection requires accurate recognition of object category, size, rotation angle, and location in 3D space. Currently, many 3D object detection methods rely on the compact Bird’s Eye View (BEV) or point-wise representations to generate proposals. However, these proposal generation paradigms neglect the spatial distribution of objects, which causes difficulty in estimating the centers of objects. In this paper, we propose a keypoint-based 3D object detector, KPDet, which employs the keypoints in the neighborhood of object centers as the representation for proposal generation. The proposed KPDet first uses voxel-keypoint mapping to aggregate informative features on the subsampled keypoints from voxel-wise features, then calibrates the misalignment between the keypoints and object centers through the Object-aware Feature Pooling (OFP) module. These aligned keypoints with their corresponding features are applied to generate proposals. Since the keypoints are essential components, we further present the Structural Point Abstraction (SPA) module, which captures the anisotropic features of keypoints via constructed structural points to enhance the geometric information. In addition, based on the well-studied multi-task learning framework, we also propose a Parametric Radius Learning (PRL) strategy to adjust the sampling radius of the point-based feature aggregation process during the training procedure. Extensive experiments on the KITI and Waymo Open Dataset show that KPDet could achieve promising results compared with previous works.