Geometry-Guided Point Generation for 3D Object Detection

Point cloud completion 3D object detectors effectively tackle the challenge of incomplete shapes in sparse point clouds by generating pseudo points to improve detection performance. However, the absence of guidance provided by the heatmap information and the geometric shape information renders the precise recovery of object shapes an arduous task. To this end, we propose a Geometry-guided Point Generation for 3D Object Detection, named GgPG. Specifically, we first design a 3D heatmap auxiliary supervision subnetwork to enhance the quality of object proposals by capturing the actual size and position of the object within the 3D heatmap representation. Moreover, we introduce a density-aware point generation module that employs Kernel Density Estimation (KDE) to embed the point density into the grid point's feature representation, thereby enabling the completion of more precise object shapes. Our GgPG achieves progressive performance in both Waymo and KITTI benchmarks, notably GgPG outperforms PGRCNN by +1.02 $\%$ , +1.18 $\%$ , and +0.56 $\%$ on the vehicle, pedestrian, and cyclist under LEVEL $\_$ 2 mAPH classes on Waymo Open Dataset, respectively.