TS-PCI: Point Cloud Frame Interpolation with Time-Aware Point Cloud Sampling and Self-Supervised Learning Strategy

Recent point cloud frame interpolation methods predict an interpolated frame through the merging of two intermediate frames constructed by scene flow estimation. However, generation errors may accumulate in the scene flow estimation errors since they adopt a generative approach to merge the frames, degrading the interpolation performance. In this paper, we propose a point cloud frame interpolation method with time-aware point cloud sampling and a self-supervised learning strategy, termed TS-PCI. The proposed method introduces a time-aware learning-based point cloud sampling model to merge the two frames into a single frame in a non-generative approach. The proposed method also introduces an attention-based geometry refinement model to improve the geometric quality of the sampled point clouds. Furthermore, the proposed method adopts a self-supervised strategy that dynamically creates ground truth labels for point cloud sampling, allowing the models to be trained in an end-to-end manner. Experimental results on three large-scale datasets show that the proposed method achieves superior performance compared to state-of-the-art methods.