AS-Det: Active Sampling for Adaptive 3D Object Detection in Point Clouds

Abstract

3D object detection in point clouds is critical in 3D computer vision, autonomous driving, and robotics. Existing point-based detectors, tailored to handle unstructured raw point clouds, often rely on simplistic sampling strategies to select a subset of points for local representation learning and detection. However, the diverse patterns exhibited by multiple types of point cloud data present a significant challenge to the universality of current detectors, particularly those captured by varied sensors (e.g., LiDAR and 4D Imaging Radar). In response to this challenge, we introduce an adaptable point-based single-stage 3D detector, AS-Det, engineered to excel on both LiDAR and 4D Radar point clouds. Specifically, we propose a novel active sampling strategy that actively mines object-related information to achieve efficient sampling and representation across different types of point clouds through end-to-end training. Additionally, we introduce a lightweight multi-scale center feature aggregation module to exploit multi-scale object context for precise and low-cost detection. By integrating the abovementioned modules, AS-Det achieves highly adaptive detection on various point clouds, encompassing different sensors and scales. Experimental results demonstrate the superior performance and adaptability of AS-Det on both LiDAR and 4D Radar point clouds.