Multi-fineness Boundaries and the Shifted Ensemble-aware Encoding for Point Cloud Semantic Segmentation

Ziming Wang, Boxiang Zhang, Ming Ma, Yue Wang, Taoli Du, Wenhui Li

Abstract: Point cloud segmentation forms the foundation of 3D scene understanding. Boundaries, the intersections of regions, are prone to mis-segmentation. Current point cloud segmentation models exhibit unsatisfactory performance on boundaries. There is limited focus on explicitly addressing semantic segmentation of point cloud boundaries. We introduce a method called Multi-fineness Boundary Constraint (MBC) to tackle this challenge. By querying boundaries at various degrees of fineness and imposing feature constraints within these boundary areas, we enhance the discrimination between boundaries and non-boundaries, improving point cloud boundary segmentation. However, solely emphasizing boundaries may compromise the segmentation accuracy in broader non-boundary regions. To mitigate this, we introduce a new concept of point cloud space termed ensemble and a Shifted Ensemble-aware Perception (SEP) module. This module establishes information interactions between points with minimal computational cost, effectively capturing direct point-to-point long-range correlations within ensembles. It enhances segmentation performance for both boundaries and non-boundaries. We conduct experiments on multiple benchmarks. The experimental results demonstrate that our method achieves performance surpassing or comparable to state-of-the-art methods, validating the effectiveness and superiority of our approach.

Primary Subject Area: [Content] Media Interpretation

Secondary Subject Area: [Experience] Interactions and Quality of Experience

Relevance To Conference: Point clouds contain diverse representations, such as spatial geometry, RGB color space, normals, time sequences, and signal intensity. These representations can be interpreted as multiple modalities, providing high-precision geometry and rich spatial structural information crucial for scene understanding. Point cloud semantic segmentation, a pivotal and foundational task in scene understanding, is closely linked with multimedia and multimodal processing. Through semantic segmentation, point cloud semantic labels are partitioned into meaningful regions or objects, facilitating the analysis and interpretation of complex spatial environments. This segmentation process fosters the development of various multimedia applications, such as augmented reality, by offering valuable spatial context and semantic information. Moreover, as a foundational algorithm, point cloud semantic segmentation can be seamlessly integrated with other modalities, including images, videos, or text data. Providing comprehensive scene understanding and semantic enhancement to other modalities improves the perception capability of multimodal scenes, enabling more accurate and robust multimedia understanding and interaction. Thus, point cloud semantic segmentation significantly advances multimedia and multimodal processing techniques, supporting crucial scene understanding and interpretation for applications such as intelligent driving, virtual reality, and environmental perception.

Submission Number: 3487