Uncertainty-Driven Gaussian Sphere Propagation for 3D Semantic Segmentation

3D point cloud semantic segmentation remains a fundamental challenge for autonomous driving and robotic perception. While Transformerbased architectures have achieved significant progress by capturing longrange dependencies, existing methods rely on deterministic point-wise predictions that fail to maintain semantic consistency across geometric voids such as occlusions and sparse boundaries. This can be attributed to the isotropic nature of conventional discrete feature aggregation, which lacks the directional awareness necessary to propagate reliable information across these discrete gaps. To address this, we propose Uncertainty-driven Gaussian Sphere Propagation (UGSP), a framework that transitions from discrete point processing to continuous geometric field reconstruction. By leveraging approximate Bayesian inference to identify reliable semantic anchors and high-uncertainty voids, a Spherical Harmonics (SH)-based aggregation mechanism is introduced that models the local scene as a collection of anisotropic Gaussian spheres. This approach enables the interpolation of semantic information along continuous spatial paths, allowing for direction-aware semantic propagation that effectively recovers structural integrity in high-uncertainty regions. Extensive experiments on indoor and outdoor benchmarks demonstrate the superiority of UGSP in semantically ambiguous and geometrically complex scenarios. The source code is publicly available at https: //github.com/LENGYI1221/UGSP.