Adaptive Annotation Distribution for Weakly Supervised Point Cloud
  Semantic Segmentation
Zhiyi Pan Nan Zhang Wei Gao Shan Liu Ge Li
Weakly supervised point cloud semantic segmentation has attracted a lot of
attention due to its ability to alleviate the heavy reliance on fine-grained
annotations of point clouds. However, in practice, sparse annotation usually
exhibits a distinct non-uniform distribution in point cloud, which poses
challenges for weak supervision. To address these issues, we propose an
adaptive annotation distribution method for weakly supervised point cloud
semantic segmentation. Specifically, we introduce the probability density
function into the gradient sampling approximation analysis and investigate the
impact of sparse annotations distributions. Based on our analysis, we propose a
label-aware point cloud downsampling strategy to increase the proportion of
annotations involved in the training stage. Furthermore, we design the
multiplicative dynamic entropy as the gradient calibration function to mitigate
the gradient bias caused by non-uniformly distributed sparse annotations and
explicitly reduce the epistemic uncertainty. Without any prior restrictions and
additional information, our proposed method achieves comprehensive performance
improvements at multiple label rates with different annotation distributions on
S3DIS, ScanNetV2 and SemanticKITTI.