publication . Article . Other literature type . 2019

SEMANTIC SEGMENTATION OF INDOOR 3D POINT CLOUD WITH SLENET

Y. Ding; Xianwei Zheng; Hanjiang Xiong; Y. Zhang;
Open Access English
  • Published: 01 Jun 2019
Abstract
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.
Subjects
ACM Computing Classification System: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Object (computer science), Discriminative model, Artificial intelligence, business.industry, business, Computer vision, Graphical model, Segmentation, Pixel, Computer science, Point cloud, lcsh:Technology, lcsh:T, lcsh:Engineering (General). Civil engineering (General), lcsh:TA1-2040, lcsh:Applied optics. Photonics, lcsh:TA1501-1820

Boulch, A., Guerry, J., Bertrand, L., Audebert, N., 2018.

SnapNet: 3D point cloud semantic labeling with 2D deep segmentation networks. Computers & Graphics, 71, 189-198.

Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A., 2018. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence, 40, 834-848.

Deng, J., Dong, W., Socher, R., Li, L., Li, K., Li, F., 2009.

Imagenet: A large-scale hierarchical image database. 2009 IEEE conference on computer vision and pattern recognition, Ieee, 248-255.

Dimitrov, A., Mani, G., 2015. Segmentation of building point cloud models including detailed architectural/structural features and MEP systems. Automation in Construction, 51, 32-45.

Fouad, I, Rady, S., Mostafa, M., 2017. Efficient image segmentation of rgb-d images. 353-358.

He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition, 770-778.

Hedau, V., Hoiem, D.and Forsyth, D., 2009. Recovering the spatial layout of cluttered rooms. 2009 IEEE 12th international conference on computer vision, IEEE, 1849-1856.

Hermans, A., Floros, G., Leibe, B., 2014. Dense 3d semantic mapping of indoor scenes from rgb-d images. 2014 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2631-2638.

Koppula, H., Anand, A., Joachims, T., Saxena, A., 2011.

Semantic labeling of 3d point clouds for indoor scenes.

Advances in neural information processing systems, 244-252.

Zhou, Y., Zheng, X., Xiong, H., Chen, R., 2017. Robust Indoor Mobile Localization with a Semantic Augmented Route Network Graph. ISPRS International Journal of Geo-Information, 6, 221.

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