Serialization based Point Cloud Oversegmentation

Point cloud oversegmentation, as a fundamental preprocess step in point cloud understanding, is a challenging task as its spatial proximity and semantic similarity requirement. Most existing works struggle to efficiently group semantically consistent points into superpoints while maintaining spatial proximity. In this paper, we propose a novel serialization based point cloud oversegmentation method, which leverages serialization to avoid complex spatial queries, directly accessing neighboring points through sequence locality for similarity matching and superpoint clustering. Specifically, we first serialize point clouds into Hilbert curve and spatially-continuously partition them into multiple initial segments. Then, to guarantee the internal semantic consistency of superpoints, we design an adaptive update algorithm that clusters superpoints by matching feature similarities between neighboring segments and updates features via Cross-Attention. Experimental results show that the proposed method achieves state-of-the-art in point cloud oversegmentation across multiple large-scale indoor and outdoor datasets. Moreover, the proposed method can be flexibly adapted to the semantic segmentation task, and achieves promising performance.