Autonomous driving paper index
DoSF-Net: Point cloud understanding via deformable offset surface-relational convolution and scale-selective fusion
One-line summary
Therefore, we propose a novel representation learning framework, termed DoSF-Net, for point cloud classification and segmentation.
Engineering notes
Key topics: autonomous driving, point cloud. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Abstract With the widespread adoption of 3D data across various scenarios, point cloud analysis remains a significant research topic in the field of 3D computer vision. Most existing studies primarily rely on spatial coordinates and their relative positional relationships to implicitly represent local geometric structures, without fully exploiting the explicit structural information within local regions. Furthermore, the differences between features at various levels in terms of geometric detail and semantic abstraction are also issues worthy of attention. Therefore, we propose a novel representation learning framework, termed DoSF-Net, for point cloud classification and segmentation. The effectiveness of DoSF-Net is primarily based on two core designs: the local structure enhancement operator, Deformable Offset Surface-Relational Convolution (DOSConv), and Scale-Selective Semantic-Geometric Fusion (SSGF). DOSConv consists of two main components: 1) Offset-guided Deformable Center Alignment (ODCA), designed to adaptively adjust local aggregation centers so that the constructed local neighborhoods better align with the actual distribution of local structures; and 2) Surface-Relational Encoding (SRE): aimed at explicitly encoding local surface relationships, more comprehensive characterization of complex local geometric structures. Our proposed Scale-Selective Semantic-Geometric Fusion promotes the complementarity of geometric details and semantic representations through scale-selective cross-layer fusion, mitigating the attenuation of geometric cues during hierarchical propagation. Experimental results on point cloud classification and segmentation datasets validate the effectiveness of our method, with DoSF-Net achieving accuracy rates of 94.1% and 86.4% on the ModelNet40 and ShapeNetPart datasets, respectively.
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