IC-FPS: Instance-Centroid Faster Point Sampling Framework for 3D Point-Based Object Detection

3D object detection is one of the most important tasks in autonomous driving and robotics. Our research focuses on tackling low efficiency issue of point-based methods, and we propose a novel Instance-Centroid Faster Point Sampling (IC- FPS) framework. We design a Neighboring Feature Diffusion Module (NFDM) to extract local features for the purpose of efficiently distinguishing the foreground from the background. Considering Farthest Point Sampling (FPS) strategy for down- sampling is computationally intensive, we propose the Centroid- Instance Sampling Strategy (CISS). CISS samples center point in large-scale point cloud by rapidly sampling the centroid and instance points of the foreground block. The proposed IC-FPS framework can be inserted into every point-based model and effectively replace the first Set Abstraction (SA) layer. Extensive experiments on several public benchmarks demonstrate the superior performance of our proposed IC- FPS. On the Waymo dataset, IC-FPS significantly improves performance of the benchmark model and increases inference speed by 3.8 times. And real-time detection of point-based methods is realized for the first time, which is meaningful for industrial applications.