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International Journal of Computer Vision

Erschienen in:

13.06.2019

Semi-supervised Semantic Mapping Through Label Propagation with Semantic Texture Meshes

verfasst von: Radu Alexandru Rosu, Jan Quenzel, Sven Behnke

Erschienen in: International Journal of Computer Vision | Ausgabe 5/2020

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Abstract

Scene understanding is an important capability for robots acting in unstructured environments. While most SLAM approaches provide a geometrical representation of the scene, a semantic map is necessary for more complex interactions with the surroundings. Current methods treat the semantic map as part of the geometry which limits scalability and accuracy. We propose to represent the semantic map as a geometrical mesh and a semantic texture coupled at independent resolution. The key idea is that in many environments the geometry can be greatly simplified without loosing fidelity, while semantic information can be stored at a higher resolution, independent of the mesh. We construct a mesh from depth sensors to represent the scene geometry and fuse information into the semantic texture from segmentations of individual RGB views of the scene. Making the semantics persistent in a global mesh enables us to enforce temporal and spatial consistency of the individual view predictions. For this, we propose an efficient method of establishing consensus between individual segmentations by iteratively retraining semantic segmentation with the information stored within the map and using the retrained segmentation to re-fuse the semantics. We demonstrate the accuracy and scalability of our approach by reconstructing semantic maps of scenes from NYUv2 and a scene spanning large buildings.

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An organized point cloud exhibits an image resembling structure, e.g. from commodity RGB-D sensors.

https://www.blender.org.

Page size was chosen based on common supported values for multiple computers used during development.

https://eval-vistas.mapillary.com/featured-challenges/1/leaderboard/1.

Acuna, D., Ling, H., Kar, A., & Fidler, S. (2018). Efficient interactive annotation of segmentation datasets with Polygon-RNN++. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 859–868).

Bao, S. Y., & Savarese, S. (2011). Semantic structure from motion. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR).

Bao, S. Y., Chandraker, M., Lin, Y., & Savarese, S. (2013). Dense object reconstruction with semantic priors. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 1264–1271).

Blaha, M., Vogel, C., Richard, A., Wegner, J. D., Pock, T., & Schindler, K. (2016). Large-scale semantic 3D reconstruction: An adaptive multi-resolution model for multi-class volumetric labeling. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 3176–3184).

Castrejon, L., Kundu, K., Urtasun, R., & Fidler, S. (2017). Annotating object instances with a Polygon-RNN. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR).

Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018). DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 834–848.CrossRef

Cherabier, I., Häne, C., Oswald, M. R., & Pollefeys, M. (2016). Multi-label semantic 3D reconstruction using voxel blocks. In Proceedings of the international conference on 3D vision (3DV) (pp. 601–610).

Cherabier, I., Schönberger, J. L., Oswald, M. R., Pollefeys, M., & Geiger, A. (2018). Learning priors for semantic 3D reconstruction. In Proceedings of the European conference on computer vision (ECCV).

Civera, J., Gálvez-López, D., Riazuelo, L., Tardós, J. D., & Montiel, J. (2011). Towards semantic SLAM using a monocular camera. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 1277–1284).

Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009). ImageNet: A large-scale hierarchical image database. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 248–255).

Douglas, D. H., & Peucker, T. K. (1973). Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica: The International Journal for Geographic Information and Geovisualization, 10(2), 112–122.CrossRef

Droeschel, D., & Behnke, S. (2018). Efficient continuous-time SLAM for 3D lidar-based online mapping. In Proceedings of the IEEE international conference on robotics and automation (ICRA).

Eigen, D., & Fergus, R. (2015). Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In Proceedings of the IEEE international conference on computer vision (ICCV) (pp. 2650–2658).

Engel, J., Schöps, T., & Cremers, D. (2014). LSD-SLAM: Large-scale direct monocular SLAM. In Proceedings of the European conference on computer vision (ECCV) (pp. 834–849).

Garland, M., & Heckbert, P. S. (1998). Simplifying surfaces with color and texture using quadric error metrics. In Proceedings of the IEEE VIS (pp. 263–269).

Goldman, D., & Chen, J. (2005). Vignette and exposure calibration and compensation. In Proceedings of the IEEE international conference on computer vision (ICCV).

Häne, C., Zach, C., Cohen, A., Angst, R., & Pollefeys, M. (2013). Joint 3D scene reconstruction and class segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 97–104).

Häne, C., Savinov, N., & Pollefeys, M. (2014). Class specific 3D object shape priors using surface normals. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 652–659).

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 770–778).

He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask R-CNN. In Proceedings of the IEEE internatioinal conference on computer vision (ICCV) (pp. 2980–2988).

Hermans, A., Floros, G., & Leibe, B. (2014). Dense 3D semantic mapping of indoor scenes from RGB-D images. In Proceedings of the IEEE international conference on robotics and automation (ICRA) (pp. 2631–2638).

Holz, D., & Behnke, S. (2015). Registration of non-uniform density 3D laser scans for mapping with micro aerial vehicles. Robotics and Autonomous Systems, 74, 318–330.CrossRef

Hornung, A., Wurm, K. M., Bennewitz, M., Stachniss, C., & Burgard, W. (2013). OctoMap: An efficient probabilistic 3D mapping framework based on octrees. Autonomous Robots, 34(3), 189–206.CrossRef

Jain, S. D., & Grauman, K. (2016). Active image segmentation propagation. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 2864–2873).

Kazhdan, M., & Hoppe, H. (2013). Screened poisson surface reconstruction. ACM Transactions on Graphics (ToG), 32(3), 29.CrossRef

Kostavelis, I., & Gasteratos, A. (2015). Semantic mapping for mobile robotics tasks: A survey. Robotics and Autonomous Systems, 66, 86–103.CrossRef

Kundu, A., Li, Y., Dellaert, F., Li, F., & Rehg, J. M. (2014). Joint semantic segmentation and 3D reconstruction from monocular video. In Proceedings of the European conference on computer vision (ECCV) (pp. 703–718).

Landrieu, L., & Simonovsky, M. (2017). Large-scale point cloud semantic segmentation with superpoint graphs. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR).

Li, X., & Belaroussi, R. (2016). Semi-dense 3D semantic mapping from monocular SLAM. arXiv preprint arXiv:1611.04144

Lianos, K. N., Schönberger, J. L., Pollefeys, M., & Sattler, T. (2018). VSO: Visual semantic odometry. In Proceedings of the European conference on computer vision (ECCV) (pp. 234–250).

Lin, G., Milan, A., Shen, C., & Reid, I. (2017). RefineNet: Multi-path refinement networks for high-resolution semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 5168–5177).

Ma, L., Stückler, J., Kerl, C., & Cremers, D. (2017). Multi-view deep learning for consistent semantic mapping with RGB-D cameras. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 598–605).

Mackowiak, R., Lenz, P., Ghori, O., Diego, F., Lange, O., & Rother, C. (2018). CEREALS—cost-effective region-based active learning for semantic segmentation. arXiv preprint arXiv:1810.09726.

Maninchedda, F., Häne, C., Jacquet, B., Delaunoy, A., & Pollefeys, M. (2016). Semantic 3D reconstruction of heads. In Proceedings of the European conference on computer vision (ECCV) (pp. 667–683).

McCormac, J., Handa, A., Davison, A., & Leutenegger, S. (2017). SemanticFusion: Dense 3D semantic mapping with convolutional neural networks. In Proceedings of the IEEE international conference on robotics and automation (ICRA) (pp. 4628–4635).

Nakajima, Y., Tateno, K., Tombari, F., & Saito, H. (2018). Fast and accurate semantic mapping through geometric-based incremental segmentation. arXiv preprint arXiv:1803.02784.

Neuhold, G., Ollmann, T., Bulo, S.R., & Kontschieder, P. (2017). The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes. In Proceedings of the IEEE international conference on computer vision (ICCV) (pp. 5000–5009).

Qi, C. R., Su, H., Mo, K., & Guibas, L. J. (2017a). PointNet: Deep learning on point sets for 3D classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR).

Qi, C. R., Yi, L., Su, H., & Guibas, L. J. (2017b). PointNet++: Deep hierarchical feature learning on point sets in a metric space. In Advances in neural information processing systems (pp. 5099–5108).

Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., & Ng, A. Y. (2009). ROS: An open-source robot operating system. In ICRA workshop on open source software.

Riegler, G., Ulusoy, A.O., & Geiger, A. (2017). OctNet: Learning deep 3D representations at high resolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR).

Ros, G., Sellart, L., Materzynska, J., Vazquez, D., & Lopez, A. M. (2016). The SYNTHIA dataset: A large collection of synthetic images for semantic segmentation of urban scenes. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 3234–3243).

Savinov, N., Häne, C., Ladicky, L., & Pollefeys, M. (2016). Semantic 3D reconstruction with continuous regularization and ray potentials using a visibility consistency constraint. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 5460–5469).

Schönberger, J. L., Pollefeys, M., Geiger, A., & Sattler, T. (2018). Semantic visual localization. CVPR.

Sheikh, R., Garbade, M., & Gall, J. (2016). Real-time semantic segmentation with label propagation. In Proceedings of the European conference on computer vision (ECCV) (pp. 3–14).

Silberman, N., Hoiem, D., Kohli, P., & Fergus, R. (2012). Indoor segmentation and support inference from RGBD images. In Proceedings of the European conference on computer vision (ECCV) (pp. 746–760).

Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556

Stueckler, J., Waldvogel, B., Schulz, H., & Behnke, S. (2014). Dense real-time mapping of object-class semantics from RGB-D video. Journal of Real-Time Image Processing (JRTIP), 10, 599–609

Su, H., Jampani, V., Deqing, S. S., Maji, E., Yang, M. H., Kautz, J., et al. (2018). SPLATNet: Sparse lattice networks for point cloud processing. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR).

Sun, L., Yan, Z., Zaganidis, A., Zhao, C., & Duckett, T. (2018). Recurrent-OctoMap: Learning state-based map refinement for long-term semantic mapping with 3D-lidar data. IEEE Robotics and Automation Letters, 3(4), 3749–3756.CrossRef

Tatarchenko, M., Park, J., Koltun, V., & Zhou, Q. Y. (2018). Tangent convolutions for dense prediction in 3D. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 3887–3896).

Tateno, K., Tombari, F., Laina, I., & Navab, N. (2017). CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction. arXiv preprint arXiv:1704.03489.

Thürrner, G., & Wüthrich, C. A. (1998). Computing vertex normals from polygonal facets. Journal of Graphics Tools, 3(1), 43–46.CrossRef

Tulsiani, S., Zhou, T., Efros, A. A., & Malik, J. (2017). Multi-view supervision for single-view reconstruction via differentiable ray consistency. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR).

Valentin, J. P., Sengupta, S., Warrell, J., Shahrokni, A., & Torr, P. H. (2013). Mesh based semantic modelling for indoor and outdoor scenes. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 2067–2074).

Vezhnevets, A., Buhmann, J. M., & Ferrari, V. (2012). Active learning for semantic segmentation with expected change. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 3162–3169).

Vineet, V., Miksik, O., Lidegaard, M., Nießner, M., Golodetz, S., Prisacariu, V. A., Kähler, O., Murray, D. W., Izadi, S., Pérez, P., et al. (2015). Incremental dense semantic stereo fusion for large-scale semantic scene reconstruction. In Proceedings of the IEEE international conference on robotics and automation (ICRA) (pp. 75–82).

Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., & Davison, A. (2015). ElasticFusion: Dense SLAM without a pose graph. In Proceedings of robotics: science and systems.

Xie, S., Girshick, R., Dollár, P., Tu, Z., & He, K. (2017). Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 5987–5995).

Yang, L., Zhang, Y., Chen, J., Zhang, S., & Chen, D. Z. (2017). Suggestive annotation: A deep active learning framework for biomedical image segmentation. In international conference on medical image computing and computer-assisted intervention (pp. 399–407).

Zaganidis, A., Sun, L., Duckett, T., & Cielniak, G. (2018). Integrating deep semantic segmentation into 3D point cloud registration. IEEE Robotics and Automation Letters, 3(4), 2942–2949.CrossRef

Zollhöfer, M., Stotko, P., Görlitz, A., Theobalt, C., Nießner, M., Klein, R., & Kolb, A. (2018). State of the art on 3D reconstruction with RGB-D cameras. In Computer graphics forum (pp. 625–652).

Titel: Semi-supervised Semantic Mapping Through Label Propagation with Semantic Texture Meshes
verfasst von: Radu Alexandru Rosu
Jan Quenzel
Sven Behnke
Publikationsdatum: 13.06.2019
Verlag: Springer US
Erschienen in: International Journal of Computer Vision / Ausgabe 5/2020
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI: https://doi.org/10.1007/s11263-019-01187-z

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