Spatiotemporal Contextual 3D Semantic Segmentation for Intelligent Outdoor Mining

Three-dimensional semantic segmentation plays a crucial role in accurately identifying terrain features and objects by effectively extracting 3D spatial information from the environment. However, the inherent sparsity of point clouds and unclear terrain boundaries in outdoor mining environments significantly complicate the recognition process. To address these challenges, we propose a novel 3D semantic segmentation network that incorporates spatiotemporal feature aggregation. Specifically, we introduced the Gated Spatiotemporal Clue Encoder, which extracts spatiotemporal context from historical multi-frame point cloud data and combines it with the current scan frame to enhance feature representation. Additionally, the Spatiotemporal Feature State Space Module is proposed to efficiently model long-term spatiotemporal features while minimizing computational and memory overhead. Experimental results show that the proposed method outperforms the baseline model, achieving a 2.1% improvement in mIoU on the self-constructed TZMD_NUC outdoor mining dataset and a 1.9% avg improvement on the public SemanticKITTI dataset. Moreover, the method simultaneously improves computational efficiency, making it more suitable for real-time applications in complex, real-world mining environments. These results validate the effectiveness of the proposed method, offering a promising solution for 3D semantic segmentation in complex, real-world mining environments, where computational efficiency and accuracy are both critical.