Weakly supervised 3D semantic segmentation has proven effective in alleviating the heavy dependence on dense annotations by generating high-quality pseudo-labels. However, due to the scene complexity and disorder of the point cloud, merely applying the model semantic prediction or hand-crafted feature similarity for pseudo labeling is inefficient and biased. This limitation inevitably results in incorrect pseudo labels. To tackle this challenge, we propose a new method called Multi-granularity Superpoint Graph Learning (MSGL) that leverages the multi-scale local features of point clouds to improve the quality of pseudo labels. We first design a multi-granularity local representation learning module on the superpoint graph to capture the neighboring structure information of each superpoint within complex scenes. Subsequently, the generated structural embedding is utilized to enhance the affinity matrix of label propagation, thereby yielding high-quality pseudo labels. To further enforce the generalization of the structural representation module under scenario changes or data fluctuations, we present a multi-granularity consistency loss in MSGL. This loss is applied across different views of the superpoint graph within each scene to ensure a robust and consistent learning process. Our experiments conducted on three benchmarks show that the proposed method outperforms existing weakly supervised methods under several sparse label settings, and improves the baseline by an average of 7.7% with only 1% extra computation cost. Moreover, our approach even compares favorably to some fully supervised methods with only one point labeled for each thing.
原文链接
分享
引用
