A Feature-Driven Approach to Semantic Segmentation in Large-Scale 3D Urban Dataset

Abstract. Urban environments are continually evolving, which presents significant challenges for 3D semantic segmentation systems that must adapt to emerging object categories. In this paper, we address the problem of Novel Class Discovery (NCD) in 3D semantic segmentation for urban scenes. We introduce a feature-driven framework that leverages the Dynamic Multi-level Feature Synthesis Module (D-MFSM) to extract and integrate multi-scale, cross-view structural information from raw urban point clouds. D-MFSM dynamically partitions point clouds via an adaptive grouping mechanism that utilizes a learnable spatial weight vector, and subsequently constructs local neighborhoods by means of an improved farthest point sampling strategy. The extracted local features are then processed by a dual-path adaptive synthesis mechanism and further refined through a novel cross-axis reordering strategy, which together yield comprehensive aggregated feature representations. These representations facilitate robust novel class discovery while maintaining high segmentation accuracy on known classes. Comprehensive evaluations on the DALES dataset demonstrate that the proposed approach yields substantial improvements in segmentation performance across diverse urban scenarios. The proposed framework, therefore, offers a complementary solution to existing methods and contributes to the development of more adaptive and accurate 3D semantic segmentation systems in complex urban settings.