Semantic Segmentation Method for Sparse Point Clouds Based on Straight Flow Completion and Multi-Feature Fusion

Point cloud semantic segmentation is a vital task in 3D computer vision. However, the inherent sparsity of point clouds complicates the segmentation process. In contexts such as autonomous driving, moving objects frequently exhibit motion blur, which adversely affects semantic segmentation performance. These challenges hinder the practical application of point cloud semantic segmentation. To address these issues, this paper presents a novel semantic segmentation method that integrates sparse point cloud completion with multi-feature fusion. Specifically, the study emphasizes the development of efficient strategies for constructing and training point cloud completion models, aiming to expedite model parameter training while maximizing completion accuracy. Additionally, a semantic segmentation model is introduced that combines motion feature-enhanced instance features with semantic features, thereby enhancing adaptability to moving objects. Moreover, point cloud completion and semantic segmentation are linked in an end-to-end pipeline, facilitating accurate semantic segmentation of sparse point clouds in dynamic environments. During the experimental phase, publicly available Lidar point cloud datasets, including SemanticKITTI and the millimeter-wave radar dataset RADIal, are utilized to evaluate the proposed method against classical approaches in terms of point cloud completion performance and semantic segmentation effectiveness, thereby demonstrating the reliability of the proposed method.