Additionally, a hash-based representation is proposed for memory-efficient spherical frustum storage.

The data is collected in Peking University and uses the same data format as SemanticKITTI. To ensure all tasks are well-defined, we formalize consistent and compatible semantic class vocabulary across the above datasets, ensuring there is a one-to-one mapping between all semantic classes. As for ASFDA and ADA settings, we have an additional warm-up stage, i.e., the network is Both source and target data have a batch size of 16. Both training loss and validation loss consistently decrease over time, indicating effective model training. We report mIoU results across existing AL approaches in Table A3.

Our initial investigation identifies which distributions accurately characterize the feature space, subsequently leveraging this priori to guide the alignment of the weakly supervised embeddings. Specifically, we analyze the superiority of the mixture of von Mises-Fisher distributions (moVMF) among several common distribution candidates.

Environmental perception systems are crucial for high-precision mapping and autonomous navigation, with LiDAR serving as a core sensor providing accurate 3D point cloud data. Efficiently processing unstructured point clouds while extracting structured semantic information remains a significant challenge. In recent years, numerous pseudo-image-based representation methods have emerged to balance efficiency and performance by fusing 3D point clouds with 2D grids. However, the fundamental inconsistency between the pseudo-image representation and the original 3D information critically undermines 2D-3D feature fusion, posing a primary obstacle for coherent information fusion and leading to poor feature discriminability. This work proposes DAGLFNet, a pseudo-image-based semantic segmentation framework designed to extract discriminative features. It incorporates three key components: first, a Global-Local Feature Fusion Encoding (GL-FFE) module to enhance intra-set local feature correlation and capture global contextual information; second, a Multi-Branch Feature Extraction (MB-FE) network to capture richer neighborhood information and improve the discriminability of contour features; and third, a Feature Fusion via Deep Feature-guided Attention (FFDFA) mechanism to refine cross-channel feature fusion precision. Experimental evaluations demonstrate that DAGLFNet achieves mean Intersection-over-Union (mIoU) scores of 69.9% and 78.7% on the validation sets of SemanticKITTI and nuScenes, respectively. The method achieves an excellent balance between accuracy and efficiency.

Additionally, a hash-based representation is proposed for memory-efficient spherical frustum storage.

Our initial investigation identifies which distributions accurately characterize the feature space, subsequently leveraging this priori to guide the alignment of the weakly supervised embeddings. Specifically, we analyze the superiority of the mixture of von Mises-Fisher distributions (moVMF) among several common distribution candidates.

The data is collected in Peking University and uses the same data format as SemanticKITTI. To ensure all tasks are well-defined, we formalize consistent and compatible semantic class vocabulary across the above datasets, ensuring there is a one-to-one mapping between all semantic classes. As for ASFDA and ADA settings, we have an additional warm-up stage, i.e., the network is Both source and target data have a batch size of 16. Both training loss and validation loss consistently decrease over time, indicating effective model training. We report mIoU results across existing AL approaches in Table A3.