Bounding Box-Guided Pseudo Point Clouds Early-Fusion and Density Optimize for 3D Object Detection

3D point clouds are used for 3D object detection. The sparsity and noises of point clouds poses a significant challenge primarily due to difficulties in capturing LiDAR data and LiDAR sensor performance. Typically, the number of captured point cloud data is lower than the real point density of real scenes. To address this issue, we adopted point clouds early-fusion, which integrates multi-modal data into a comprehensive 3D point clouds. We propose a straightforward and efficient pseudo-point cloud generation method that can be easily integrated with existing point cloud data. This involves generating pseudo point clouds and then fusing them with the original point cloud data. We also propose vertical density adaptation method to address the problem of density inhomogeneity in point cloud data following early-fusion. We conduct experiments on the well-known KITTI dataset using three state-of-the-art LiDAR 3D object detectors. Our results show that our fusion strategy can enhance the performance of existing detectors, which could potentially inspire future detector designs.