The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W13
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 785–791, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-785-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 785–791, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-785-2019

  05 Jun 2019

05 Jun 2019

SEMANTIC SEGMENTATION OF INDOOR 3D POINT CLOUD WITH SLENET

Y. Ding1, X. Zheng1, H. Xiong1, and Y. Zhang2 Y. Ding et al.
  • 1State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, WuHan University, Hubei, Wuhan, China
  • 2School of Mathematics and Statistics, Wuhan University, Hubei, Wuhan, China

Keywords: Indoor Mapping, 2D-3D Semantic Label Propagation, Semantic Segmentation, 3D Point Cloud

Abstract. With the rapid development of new indoor sensors and acquisition techniques, the amount of indoor three dimensional (3D) point cloud models was significantly increased. However, these massive “blind” point clouds are difficult to satisfy the demand of many location-based indoor applications and GIS analysis. The robust semantic segmentation of 3D point clouds remains a challenge. In this paper, a segmentation with layout estimation network (SLENet)-based 2D–3D semantic transfer method is proposed for robust segmentation of image-based indoor 3D point clouds. Firstly, a SLENet is devised to simultaneously achieve the semantic labels and indoor spatial layout estimation from 2D images. A pixel labeling pool is then constructed to incorporate the visual graphical model to realize the efficient 2D–3D semantic transfer for 3D point clouds, which avoids the time-consuming pixel-wise label transfer and the reprojection error. Finally, a 3D-contextual refinement, which explores the extra-image consistency with 3D constraints is developed to suppress the labeling contradiction caused by multi-superpixel aggregation. The experiments were conducted on an open dataset (NYUDv2 indoor dataset) and a local dataset. In comparison with the state-of-the-art methods in terms of 2D semantic segmentation, SLENet can both learn discriminative enough features for inter-class segmentation while preserving clear boundaries for intra-class segmentation. Based on the excellence of SLENet, the final 3D semantic segmentation tested on the point cloud created from the local image dataset can reach a total accuracy of 89.97%, with the object semantics and indoor structural information both expressed.