  This paper investigates the indistinguishable points (difficult to predict
label) in semantic segmentation for large-scale 3D point clouds. The
indistinguishable points consist of those located in complex boundary, points
with similar local textures but different categories, and points in isolate
small hard areas, which largely harm the performance of 3D semantic
segmentation. To address this challenge, we propose a novel Indistinguishable
Area Focalization Network (IAF-Net), which selects indistinguishable points
adaptively by utilizing the hierarchical semantic features and enhances
fine-grained features for points especially those indistinguishable points. We
also introduce multi-stage loss to improve the feature representation in a
progressive way. Moreover, in order to analyze the segmentation performances of
indistinguishable areas, we propose a new evaluation metric called
Indistinguishable Points Based Metric (IPBM). Our IAF-Net achieves the
comparable results with state-of-the-art performance on several popular 3D
point cloud datasets e.g. S3DIS and ScanNet, and clearly outperforms other
methods on IPBM.
