Enhancing semantic segmentation with detection priors and iterated graph cuts for robotics

Abdulla, 2017

Anand, 2013, Contextually guided semantic labeling and search for three-dimensional point clouds, Int. J. Robot. Res., 32, 19, 10.1177/0278364912461538

Antonello, 2018, Multi-view 3d entangled forest for semantic segmentation and mapping, 1855

Capobianco, 2015, A proposal for semantic map representation and evaluation, 1

Carraro, 2015, An open source robotic platform for ambient assisted living, Artif. Intell. Robot. (AIRO)

Chen, 2019, Multi-view incremental segmentation of 3-d point clouds for mobile robots, IEEE Robot. Autom. Lett., 4, 1240, 10.1109/LRA.2019.2894915

Chen, Liang-Chieh, Zhu, Yukun, Papandreou, George, Schroff, Florian, Hartwig, Adam, 2018. Encoder–decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801–818.

Couprie, 2013, Indoor semantic segmentation using depth information, 1

Eigen, David, Fergus, Rob, 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.

Endres, 2014, 3-d mapping with an rgb-d camera, IEEE Trans. Robot., 30, 177, 10.1109/TRO.2013.2279412

Farabet, 2013, Learning hierarchical features for scene labeling, IEEE Trans. Pattern Anal. Mach. Intell., 35, 1915, 10.1109/TPAMI.2012.231

Felzenszwalb, 2010, Object detection with discriminatively trained part-based models, IEEE Trans. Pattern Anal. Mach. Intell., 32, 1627, 10.1109/TPAMI.2009.167

Fischinger, 2016, Hobbit, a care robot supporting independent living at home: First prototype and lessons learned, Robot. Auton. Syst., 75, 60, 10.1016/j.robot.2014.09.029

Girshick, 2014, Rich feature hierarchies for accurate object detection and semantic segmentation

Gupta, 2015, Indoor scene understanding with rgb-d images: Bottom-up segmentation, object detection and semantic segmentation, Int. J. Comput. Vis., 112, 133, 10.1007/s11263-014-0777-6

Handa, Ankur, Patraucean, Viorica, Badrinarayanan, Vijay, Stent, Simon, Cipolla, Roberto, 2016. Understanding real world indoor scenes with synthetic data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4077–4085.

He, 2017, Std2p: Rgbd semantic segmentation using spatio-temporal data-driven pooling, 7158

He, K., Gkioxari, G., Dollár, P., Girshick, R., 2017b. Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 2961–2969.

Hermans, 2014, Dense 3d semantic mapping of indoor scenes from rgb-d images, 2631

Hosang, 2016, What makes for effective detection proposals?, IEEE Trans. Pattern Anal. Mach. Intell., 38, 814, 10.1109/TPAMI.2015.2465908

Hosang, 2014

Kanezaki, 2015, 3d selective search for obtaining object candidates, 82

Karpathy, 2015, Deep visual-semantic alignments for generating image descriptions

Kostavelis, 2012, On the optimization of hierarchical temporal memory, Pattern Recognit. Lett., 33, 670, 10.1016/j.patrec.2011.11.017

Kostavelis, 2013, Learning spatially semantic representations for cognitive robot navigation, Robot. Auton. Syst., 61, 1460, 10.1016/j.robot.2013.07.008

Kostavelis, 2015, Semantic mapping for mobile robotics tasks: A survey, Robot. Auton. Syst., 66, 86, 10.1016/j.robot.2014.12.006

Kostavelis, 2017, Semantic maps from multiple visual cues, Expert Syst. Appl., 68, 45, 10.1016/j.eswa.2016.10.014

Lempitsky, 2009, Image segmentation with a bounding box prior, 277

Lin, 2013

Lin, 2014, Microsoft coco: Common objects in context, 740

Liu, 2016, Ssd: Single shot multibox detector, 21

Ma, 2017, Multi-view deep learning for consistent semantic mapping with rgb-d cameras, 598

Maninis, 2018, Deep extreme cut: From extreme points to object segmentation

McCormac, 2016

Nakajima, 2018, Efficient object-oriented semantic mapping with object detector, IEEE Access, 7, 3206, 10.1109/ACCESS.2018.2887022

Nakajima, 2018, Fast and accurate semantic mapping through geometric-based incremental segmentation, 385

Nüchter, 2008, Towards semantic maps for mobile robots, Robot. Auton. Syst., 56, 915, 10.1016/j.robot.2008.08.001

Papageorgiou, 2000, A trainable system for object detection, Int. J. Comput. Vis., 38, 15, 10.1023/A:1008162616689

Papon, J., Abramov, A., Schoeler, M., Worgotter, F., 2013. Voxel cloud connectivity segmentation-supervoxels for point clouds. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2027–2034.

Pronobis, 2012, Large-scale semantic mapping and reasoning with heterogeneous modalities, 3515

Redmon, Joseph, Divvala, Santosh, Girshick, Ross, Farhadi, Ali, 2016. You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788.

Redmon, 2016

Redmon, 2018

Ren, 2015, Faster R-CNN: Towards real-time object detection with region proposal networks

Rother, 2004, Grabcut: Interactive foreground extraction using iterated graph cuts, ACM Trans. Graph., 23, 309, 10.1145/1015706.1015720

Russell, 2008, Labelme: a database and web-based tool for image annotation, Int. J. Comput. Vis., 77, 157, 10.1007/s11263-007-0090-8

Salas-Moreno, Renato F., Newcombe, Richard A., Strasdat, Hauke, Kelly, Paul H.J., Davison, Andrew J., 2013. Slam++: Simultaneous localisation and mapping at the level of objects. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1352–1359.

Siam, Mennatullah, Gamal, Mostafa, Abdel-Razek, Moemen, Yogamani, Senthil, Jagersand, Martin, Zhang, Hong, 2018. A comparative study of real-time semantic segmentation for autonomous driving. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshop), pp. 587–597.

Silberman, 2011, Indoor scene segmentation using a structured light sensor, 601

Silberman, 2012, Indoor segmentation and support inference from rgbd images, 746

Stückler, 2012, Semantic mapping using object-class segmentation of rgb-d images, 3005

Sturgess, 2009, Combining appearance and structure from motion features for road scene understanding

Sünderhauf, 2017, Meaningful maps with object-oriented semantic mapping, 5079

Szegedy, Christian, Liu, Wei, Jia, Yangqing, Sermanet, Pierre, Reed, Scott, Anguelov, Dragomir, Erhan, Dumitru, Vanhoucke, Vincent, Rabinovich, Andrew, 2015. Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9.

Tchapmi, 2017, Segcloud: Semantic segmentation of 3d point clouds, 537

Terreran, 2019, Boat hunting with semantic segmentation for flexible and autonomous manufacturing, 1

Uijlings, 2013, Selective search for object recognition, Int. J. Comput. Vis., 104, 154, 10.1007/s11263-013-0620-5

Vanhoey, 2017, Varcity-the video: the struggles and triumphs of leveraging fundamental research results in a graphics video production, 48

Vincze, 2016, Learning and detecting objects with a mobile robot to assist older adults in their homes, 316

Viola, 2004, Robust real-time face detection, Int. J. Comput. Vis., 57, 137, 10.1023/B:VISI.0000013087.49260.fb

Wang, 2019

Wolf, 2015, Fast semantic segmentation of 3d point clouds using a dense crf with learned parameters, 4867

Wolf, 2016, Enhancing semantic segmentation for robotics: the power of 3-d entangled forests, IEEE Robot. Autom. Lett., 1, 49, 10.1109/LRA.2015.2506118