Robust Vegetation Filtering for Rock-Mass Scene Point Clouds via Bidirectional Mamba and Adaptive Triplane Feature Representation

The irregular and chaotically interwoven distributions of vegetation and rock mass in natural environments pose significant challenges for vegetation filtering of rock point clouds, as existing methods struggle with inefficient contextual modeling and insufficient feature discriminability. To address these limitations, we propose a robust vegetation filtering method for rock mass scene point clouds featuring two key innovations: (1) Bidirectional Point Cloud Mamba module that adopts an alternating allocation strategy to divide the sampled superpoints into two complementary groups, and constructs optimized sequences from edge to center for each group via the Traveling Salesman Problem (TSP) algorithm to enable spatially continuous context capture while eliminating dependency on regular geometric priors; (2) Structure-based adaptive Tri-Plane feature representation includes Principal Component Analysis (PCA)-based support plane selection, projective 2D feature mapping, and learnable multi-scale aggregation. By exploiting the distribution differences of rock mass and vegetation structure information in 2D projections while emulating human visual perception mechanisms that rely on optimal viewing perspectives and different scales, this module enables more discriminative feature extraction in complex natural rock-mass scenarios. Extensive comparative and ablation experiments on real-world datasets demonstrate that our method achieves superior accuracy, robustness, and generalization, and excels in preserving intricate details compared to existing methods.