Point cloud densification is essential for understanding the 3D environment. It provides crucial structural and semantic information for downstream tasks such as 3D object detection and tracking. However, existing registration-based methods struggle with dynamic targets due to the incompleteness and deformation of point clouds. To address this challenge, we propose a Kalman-based scene flow estimation method for point cloud densification and 3D object detection in dynamic scenes. Our method effectively tackles the issue of localization errors in scene flow estimation and enhances the accuracy and precision of shape completion. Specifically, we introduce a Kalman filter to correct the dynamic target’s position while estimating long sequence scene flow. This approach helps eliminate the cumulative localization error during the scene flow estimation process. Extended experiments on the KITTI 3D tracking dataset demonstrate that our method significantly improves the performance of LiDAR-only detectors, achieving superior results compared to the baselines.

Optical information synthesis, which fuses LiDAR and optical cameras, has the potential for highly detailed 3D representations. However, due to the disparity of information density between point clouds and images, conventional matching methods based on points often lose significant information. To address this issue, we propose a regional matching method to bridge the differences in information density between point clouds and images. In detail, fine semantic regions are extracted from images by analyzing their gradients. Simultaneously, point clouds are transformed into meshes, where each facet corresponds to a coarse semantic region. Extrinsic matrices are used to unify the point cloud coordinate system with the image coordinate system. The mesh is further subdivided based on the guidance of image texture information to create regional matching units. Within each matching unit, the information density of the point cloud and the image is carefully balanced at a semantic level. The texture features of the image are well preserved in the transformed mesh structure. Consequently, the proposed texture synthesis method significantly enhances the overall quality and realism of the 3D imaging.