# Investigate Indistinguishable Points in Semantic Segmentation of 3D Point Cloud Source: [Investigate Indistinguishable Points in Semantic Segmentation of 3D Point Cloud]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:09:36.045Z Authors: Mingye Xu, Zhipeng Zhou, Junhao Zhang, Yu Qiao Published: 2021-03-18T15:54:59.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Semantic Segmentation on S3DIS (Area 5)](): Semantic segmentation; S3DIS (Area 5); mIOU: 64.6. ## Original sources and implementation [Original paper]() [PDF]()