Unsupervised feature learning for 3D scene labeling
Abstract
This paper presents an approach for labeling objects in 3D scenes. We introduce HMP3D, a hierarchical sparse coding technique for learning features from 3D point cloud data. HMP3D classifiers are trained using a synthetic dataset of virtual scenes generated using CAD models from an online database. Our scene labeling system combines features learned from raw RGB-D images and 3D point clouds directly, without any hand-designed features, to assign an object label to every 3D point in the scene. Experiments on the RGB-D Scenes Dataset v.2 demonstrate that the proposed approach can be used to label indoor scenes containing both small tabletop objects and large furniture pieces.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Lai et al. build a 3D map from RGB-D sequences to then carry out an offline object classification.
“Similarly, Lai et al. [148] build a 3D map from RGB-D sequences to then carry out an offline object classification.”
From Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016) · cites this paper as [148]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
$rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
Aharon, M., Elad, M., & Bruckstein, A. M. (2006) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2006.881199 · Cited by 9,464 · see citing works
Fast approximate energy minimization via graph cuts
Boykov, Y., Veksler, O., & Zabih, R. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.969114 · Cited by 6,988 · see citing works
LIBLINEAR: A Library for Large Linear Classification
Fan, R.-E., Chang, K., Hsieh, C., Xiang-rui, W., & Lin, C. (2008) · doi:10.5555/1390681.1442794 · Cited by 6,680 · see citing works
KinectFusion: Real-time dense surface mapping and tracking
Newcombe, R. A., Fitzgibbon, A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A. J., Kohi, P., Shotton, J., & Hodges, S. (2011) · 2011 10th IEEE International Symposium on Mixed and Augmented Reality · doi:10.1109/ismar.2011.6092378 · Cited by 4,008 · see citing works
Linear spatial pyramid matching using sparse coding for image classification
Yang, J., Yu, K., Gong, Y., & Huang, T. S. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206757 · Cited by 2,895 · see citing works
Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553453 · Cited by 2,412 · see citing works
Real-time human pose recognition in parts from single depth images
Shotton, J., Sharp, T., Kipman, A., Fitzgibbon, A. W., Finocchio, M., Blake, A., Cook, M., & Moore, R. (2013) · Communications of the ACM · doi:10.1145/2398356.2398381 · Cited by 2,050 · see citing works
Make3D: Learning 3D Scene Structure from a Single Still Image
Saxena, A., Sun, M., & Ng, A. Y. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.132 · Cited by 1,738 · see citing works
Shilane, P., Min, P., Kazhdan, M., & Funkhouser, T. A. (2004) · IEEE International Conference on Shape Modeling and Applications · doi:10.1109/smi.2004.1314504 · Cited by 1,461 · see citing works
A large-scale hierarchical multi-view RGB-D object dataset
Lai, K., Bo, L., Ren, X., & Fox, D. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5980382 · Cited by 1,339 · see citing works
Self-citationRotation invariant spherical harmonic representation of 3D shape descriptors
Kazhdan, M., Funkhouser, T. A., & Rusinkiewicz, S. M. (2003) · doi:10.5555/882370.882392 · Cited by 1,228 · see citing works
(2011) · Cited by 1,192 · see citing works
RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments
Henry, P., Krainin, M., Herbst, E. V., Ren, X., & Fox, D. (2013) · Springer tracts in advanced robotics · doi:10.1007/978-3-642-28572-1_33 · Cited by 810 · see citing works
Self-citationUnsupervised Feature Learning for RGB-D Based Object Recognition
Bo, L., Ren, X., & Fox, D. (2013) · Springer tracts in advanced robotics · doi:10.1007/978-3-319-00065-7_27 · Cited by 362 · see citing works
Self-citationSemantic Labeling of 3D Point Clouds for Indoor Scenes
Koppula, H. S., Anand, A., Joachims, T., & Saxena, A. (2011) · neural information processing systems · Cited by 322 · see citing works
Object recognition and full pose registration from a single image for robotic manipulation
Collet, A., Berenson, D., Srinivasa, S. S., & Ferguson, D. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2009.5152739 · Cited by 277 · see citing works
Histograms of Sparse Codes for Object Detection
Ren, X., & Ramanan, D. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.417 · Cited by 239 · see citing works
Contextually guided semantic labeling and search for three-dimensional point clouds
Anand, A., Koppula, H. S., Joachims, T., & Saxena, A. (2012) · The International Journal of Robotics Research · doi:10.1177/0278364912461538 · Cited by 216 · see citing works
Multipath Sparse Coding Using Hierarchical Matching Pursuit
Bo, L., Ren, X., & Fox, D. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.91 · Cited by 202 · see citing works
Self-citationSparse distance learning for object recognition combining RGB and depth information
Lai, K., Bo, L., Ren, X., & Fox, D. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5980377 · Cited by 191 · see citing works
Self-citationA learned feature descriptor for object recognition in RGB-D data
Blum, M., Springenberg, J. T., Wülfing, J., & Riedmiller, M. (2012) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2012.6225188 · Cited by 190 · see citing works
Hierarchical Matching Pursuit for Image Classification: Architecture and Fast Algorithms
Bo, L., Ren, X., & Fox, D. (2011) · Cited by 187 · see citing works
Self-citationDetection-based object labeling in 3D scenes
Lai, K., Bo, L., Ren, X., & Fox, D. (2012) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2012.6225316 · Cited by 162 · see citing works
Self-citationObject Recognition in 3D Point Clouds Using Web Data and Domain Adaptation
Lai, K., & Fox, D. (2010) · The International Journal of Robotics Research · doi:10.1177/0278364910369190 · Cited by 152 · see citing works
Self-citation3-D scene analysis via sequenced predictions over points and regions
Xiong, X., Munoz, D., Bagnell, J. A., & Hebert, M. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5980125 · Cited by 133 · see citing works
High-accuracy 3D sensing for mobile manipulation: Improving object detection and door opening
Quigley, M., Batra, S., Gould, S. J., Klingbeil, E., Le, Q. V., Wellman, A., & Ng, A. Y. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2009.5152750 · Cited by 91 · see citing works
Patch Volumes: Segmentation-Based Consistent Mapping with RGB-D Cameras
Henry, P., Fox, D., Bhowmik, A. K., & Mongia, R. K. (2013) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2013.59 · Cited by 83 · see citing works
Self-citationBrowatzki, B., Fischer, J., Graf, B., Bülthoff, H., & Wallraven, C. (2011) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2011.6130385 · Cited by 71 · see citing works
Monte Carlo Pose Estimation with Quaternion Kernels and the Bingham Distribution
Glover, J., Rusu, R. B., & Bradski, G. R. (2011) · Robotics: Science and Systems · doi:10.15607/rss.2011.vii.015 · Cited by 71 · see citing works
Instance-based AMN classification for improved object recognition in 2D and 3D laser range data
Triebel, R., Schmidt, R. K., Mozos, Ó. M., & Burgard, W. (2007) · Lincoln Repository (University of Lincoln) · Cited by 54 · see citing works
Attribute based object identification
Sun, Y., Bo, L., & Fox, D. (2013) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2013.6630858 · Cited by 53 · see citing works
Self-citationEfficient 3-D scene analysis from streaming data
Hu, H., Munoz, D., Bagnell, J. A., & Hebert, M. (2013) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2013.6630888 · Cited by 47 · see citing works
Scene parsing using a prior world model
Hager, G. D., & Wegbreit, B. (2011) · The International Journal of Robotics Research · doi:10.1177/0278364911399340 · Cited by 28 · see citing works
Monte Carlo Pose Estimation with Quaternion Kernels and the Bingham Distribution
Glover, J., Bradski, G. R., & Rusu, R. B. (2012) · The MIT Press eBooks · doi:10.7551/mitpress/9481.003.0018 · Cited by 22 · see citing works
Harmonic Shape Histograms for 3D Shape Classification and Retrieval.
Fehr, J., & Burkhardt, H. (2007) · Machine Vision and Applications · Cited by 7 · see citing works
Flagged first, then by citations
5 of the 41 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
VoxNet: A 3D Convolutional Neural Network for real-time object recognition
Maturana, D., & Scherer, S. (2015) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353481 · Cited by 3,674 · see citing works
Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., & Leonard, J. J. (2016) · IEEE Transactions on Robotics · doi:10.1109/tro.2016.2624754 · Cited by 3,596 · see citing works
Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I. D., & Leonard, J. J. (2016) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-138892 · Cited by 3,094 · see citing works
FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation
Yang, Y. L., Feng, C., Shen, Y., & Tian, D. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00029 · Cited by 1,365 · see citing works
Semantic Scene Completion from a Single Depth Image
Song, S., Yu, F., Zeng, A., Chang, A. X., Savva, M., & Funkhouser, T. A. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.28 · Cited by 1,244 · see citing works
3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions
Zeng, A., Song, S., Nießner, M., Fisher, M., Xiao, J., & Funkhouser, T. A. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.29 · Cited by 1,132 · see citing works
Li, J., Skinner, K. A., Eustice, R. M., & Johnson-Roberson, M. (2017) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2017.2730363 · Cited by 837 · see citing works
CNN-SLAM: Real-Time Dense Monocular SLAM with Learned Depth Prediction
Tateno, K., Tombari, F., Laina, I., & Navab, N. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.695 · Cited by 800 · see citing works
Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images
Song, S., & Xiao, J. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.94 · Cited by 671 · see citing works
PPFNet: Global Context Aware Local Features for Robust 3D Point Matching
Deng, H., Birdal, T., & Ilić, S. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00028 · Cited by 631 · see citing works
Ego4D: Around the World in 3,000 Hours of Egocentric Video
Grauman, K., Westbury, A., Byrne, E., Chavis, Z., Furnari, A., Girdhar, R., Hamburger, J., Jiang, H., Liu, M., Liu, X., et al. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01842 · Cited by 630 · see citing works
Vehicle Detection from 3D Lidar Using Fully Convolutional Network
Li, B., Zhang, T., & Xia, T. (2016) · Robotics: Science and Systems XII · doi:10.15607/rss.2016.xii.042 · Cited by 577 · see citing works
The Perfect Match: 3D Point Cloud Matching With Smoothed Densities
Gojčič, Ž., Zhou, C., Wegner, J. D., & Wieser, A. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00569 · Cited by 530 · see citing works
3D fully convolutional network for vehicle detection in point cloud
Li, B. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8205955 · Cited by 488 · see citing works
InLoc: Indoor Visual Localization with Dense Matching and View Synthesis
Taira, H., Okutomi, M., Sattler, T., Cimpoi, M., Pollefeys, M., Šivic, J., Pajdla, T., & Torii, A. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00752 · Cited by 481 · see citing works
SnapNet: 3D point cloud semantic labeling with 2D deep segmentation networks
Boulch, A., Guerry, J., Saux, B. L., & Audebert, N. (2017) · Computers & Graphics · doi:10.1016/j.cag.2017.11.010 · Cited by 405 · see citing works
PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors
Deng, H., Birdal, T., & Ilić, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01228-1_37 · Cited by 406 · see citing works
Fernando, T., Denman, S., Sridharan, S., & Fookes, C. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.09.002 · Cited by 375 · see citing works
FAST SEMANTIC SEGMENTATION OF 3D POINT CLOUDS WITH STRONGLY VARYING DENSITY
Häckel, T., Wegner, J. D., & Schindler, K. (2016) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-iii-3-177-2016 · Cited by 373 · see citing works
SceneNN: A Scene Meshes Dataset with aNNotations
Hua, B., Pham, Q.-H., Nguyen, D. T., Tran, M.-K., Yu, L.-F., & Yeung, S.-K. (2016) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.18 · Cited by 347 · see citing works
Diving deeper into underwater image enhancement: A survey
Anwar, S., & Li, C. (2020) · Signal Processing Image Communication · doi:10.1016/j.image.2020.115978 · Cited by 325 · see citing works
VConv-DAE: Deep Volumetric Shape Learning Without Object Labels
Sharma, A., Grau, O., & Fritz, M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-49409-8_20 · Cited by 266 · see citing works
Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds
Engelmann, F., Kontogianni, T., Hermans, A., & Leibe, B. (2017) · RWTH Publications (RWTH Aachen) · Cited by 254 · see citing works
Synthesizing Training Data for Object Detection in Indoor Scenes
Georgakis, G., Mousavian, A., Berg, A. C., & Košecká, J. (2017) · Robotics: Science and Systems · doi:10.15607/rss.2017.xiii.043 · Cited by 231 · see citing works
Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds
Engelmann, F., Kontogianni, T., Hermans, A., & Leibe, B. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.90 · Cited by 280 · see citing works
3D Convolutional Neural Networks for landing zone detection from LiDAR
Maturana, D., & Scherer, S. (2015) · 2015 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2015.7139679 · Cited by 203 · see citing works
Aligning 3D models to RGB-D images of cluttered scenes
Gupta, S., Arbeláez, P., Girshick, R., & Malik, J. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299105 · Cited by 199 · see citing works
A dataset for developing and benchmarking active vision
Ammirato, P., Poirson, P., Park, E., Kosecka, J., & Berg, A. C. (2017) · 2017 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2017.7989164 · Cited by 187 · see citing works
Structured Prediction of Unobserved Voxels from a Single Depth Image
Firman, M., Aodha, O. M., Julier, S., & Brostow, G. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.586 · Cited by 178 · see citing works
Virtual Multi-view Fusion for 3D Semantic Segmentation
Kundu, A., Yin, X., Fathi, A., Ross, D. A., Brewington, B. E., Funkhouser, T. A., & Pantofaru, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58586-0_31 · Cited by 169 · see citing works
RGBD Datasets: Past, Present and Future
Firman, M. (2016) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2016.88 · Cited by 150 · see citing works
RoReg: Pairwise Point Cloud Registration With Oriented Descriptors and Local Rotations
Wang, H., Liu, Y., Hu, Q., Wang, B., Chen, J., Dong, Z., Guo, Y., Wang, W., & Yang, B. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3244951 · Cited by 142 · see citing works
Learning Local Shape Descriptors from Part Correspondences with Multiview Convolutional Networks
Huang, H., Kalogerakis, E., Chaudhuri, S., Ceylan, D., Kim, V. G., & Yumer, E. (2017) · ACM Transactions on Graphics · doi:10.1145/3137609 · Cited by 139 · see citing works
Learning scene-specific pedestrian detectors without real data
Hattori, H., Boddeti, V. N., Kitani, K., & Takeo Kanade, A. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299006 · Cited by 134 · see citing works
Unseen Object Instance Segmentation for Robotic Environments
Xie, C., Yu, X., Mousavian, A., & Fox, D. (2021) · IEEE Transactions on Robotics · doi:10.1109/tro.2021.3060341 · Cited by 175 · see citing works
Self-citationEnd-to-End Learning Local Multi-View Descriptors for 3D Point Clouds
Li, L., Zhu, S., Fu, H., Tan, P., & Tai, C. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00199 · Cited by 126 · see citing works
DA-RNN: Semantic Mapping with Data Associated Recurrent Neural Networks
Yu, X., & Fox, D. (2017) · Robotics: Science and Systems · doi:10.15607/rss.2017.xiii.013 · Cited by 119 · see citing works
Self-citationA novel weakly-supervised approach for RGB-D-based nuclear waste object detection
Sun, L., Zhao, C., Yan, Z., Liu, P., Duckett, T., & Stolkin, R. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2018.2888815 · Cited by 119 · see citing works
Chen, K., Lai, Y., Wu, Y., Martin, R. R., & Hu, S. (2014) · ACM Transactions on Graphics · doi:10.1145/2661229.2661239 · Cited by 113 · see citing works
Neural Graph Matching Networks for Fewshot 3D Action Recognition
Guo, M., Chou, E., Huang, D.-A., Song, S., Yeung, S. Y., & Fei-Fei, L. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01246-5_40 · Cited by 107 · see citing works
Data-driven shape analysis and processing
Xu, K., Kim, V. G., Qi-xing Huang, A., Mitra, N. J., & Kalogerakis, E. (2016) · SIGGRAPH Asia Courses · doi:10.1145/2988458.2988473 · Cited by 104 · see citing works
InLoc: Indoor Visual Localization with Dense Matching and View Synthesis
Taira, H., Okutomi, M., Sattler, T., Cimpoi, M., Pollefeys, M., Šivic, J., Pajdla, T., & Torii, A. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2952114 · Cited by 111 · see citing works
Two Stream 3D Semantic Scene Completion
Garbade, M., Chen, Y.-T., Sawatzky, J., & Gall, J. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00055 · Cited by 99 · see citing works
3D indoor scene modeling from RGB-D data: A survey
Chen, K., Lai, Y., & Hu, S. (2015) · Computational Visual Media · doi:10.1007/s41095-015-0029-x · Cited by 98 · see citing works
Know What Your Neighbors Do : 3D Semantic Segmentation of Point Clouds
Engelmann, F., Kontogianni, T., Schult, J., & Leibe, B. (2019) · RWTH Publications (RWTH Aachen) · Cited by 96 · see citing works
DeepVideoMVS: Multi-View Stereo on Video with Recurrent Spatio-Temporal Fusion
Düzçeker, A., Galliani, S., Vogel, C., Speciale, P., Dusmanu, M., & Pollefeys, M. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01507 · Cited by 96 · see citing works
First-person pose recognition using egocentric workspaces
Rogez, G., Supančič, J. S., & Ramanan, D. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299061 · Cited by 94 · see citing works
When 2.5D is not enough: Simultaneous reconstruction, segmentation and recognition on dense SLAM
Tateno, K., Tombari, F., & Navab, N. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487378 · Cited by 92 · see citing works
Multi-view manifold learning with locality alignment
Zhao, Y., You, X., Yu, S., Xu, C., Yuan, W., Jing, X., Zhang, T., & Tao, D. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.01.012 · Cited by 85 · see citing works
Rafique, A. A., Jalal, A., & Kim, K. (2020) · Symmetry · doi:10.3390/sym12111928 · Cited by 85 · see citing works
A deep journey into image enhancement: A survey of current and emerging trends
Lepcha, D. C., Goyal, B., Dogra, A., Sharma, K. P., & Gupta, D. N. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.12.012 · Cited by 90 · see citing works
Cross-modal adaptation for RGB-D detection
Hoffman, J., Gupta, S., Leong, J., Guadarrama, S., & Darrell, T. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487708 · Cited by 83 · see citing works
Hand-Object Contact Force Estimation from Markerless Visual Tracking
Pham, T.-H., Kyriazis, N., Argyros, A., & Kheddar, A. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2759736 · Cited by 83 · see citing works
SnapNet-R: Consistent 3D Multi-view Semantic Labeling for Robotics
Guerry, J., Boulch, A., Saux, B. L., Moras, J., Plyer, A., & Filliat, D. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.85 · Cited by 82 · see citing works
MVTec D2S: Densely Segmented Supermarket Dataset
Follmann, P., Böttger, T., Härtinger, P., König, R., & Ulrich, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01249-6_35 · Cited by 81 · see citing works
Robot@Home, a robotic dataset for semantic mapping of home environments
Ruiz-Sarmiento, J.-R., Galindo, C., & González-Jiménez, J. (2017) · The International Journal of Robotics Research · doi:10.1177/0278364917695640 · Cited by 81 · see citing works
Semantic segmentation of 3D textured meshes for urban scene analysis
Rouhani, M., Lafarge, F., & Alliez, P. (2016) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2016.12.001 · Cited by 77 · see citing works
Learning 3D Semantic Segmentation with only 2D Image Supervision
Genova, K., Yin, X., Kundu, A., Pantofaru, C., Cole, F. H., Sud, A., Brewington, B. E., Shucker, B., & Funkhouser, T. A. (2021) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv53792.2021.00046 · Cited by 76 · see citing works
Rafique, A. A., Jalal, A., & Kim, K. (2020) · International Bhurban Conference on Applied Sciences and Technology (IBCAST) · doi:10.1109/ibcast47879.2020.9044576 · Cited by 74 · see citing works
Multiview RGB-D Dataset for Object Instance Detection
Georgakis, G., Reza, M. A., Mousavian, A., Le, P.-H., & Košecká, J. (2016) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.52 · Cited by 68 · see citing works
Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation
Zhang, J., Zhu, C., Zheng, L., & Xu, K. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00459 · Cited by 69 · see citing works
KSS-ICP: Point Cloud Registration Based on Kendall Shape Space
Lv, C., Lin, W., & Zhao, B. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3251021 · Cited by 68 · see citing works
Hierarchical Semantic Labeling for Task-Relevant RGB-D Perception
Wu, C., Lenz, I., & Saxena, A. (2014) · Robotics: Science and Systems Conference · doi:10.15607/rss.2014.x.006 · Cited by 72 · see citing works
Joint classification and contour extraction of large 3D point clouds
Häckel, T., Wegner, J. D., & Schindler, K. (2017) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2017.05.012 · Cited by 64 · see citing works
A Multi-Modal, Discriminative and Spatially Invariant CNN for RGB-D Object Labeling
Asif, U., Bennamoun, M., & Sohel, F. A. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2747134 · Cited by 61 · see citing works
A Survey of 3D Indoor Scene Synthesis
Zhang, S.-H., Zhang, S.-K., Liang, Y., & Hall, P. M. (2019) · Journal of Computer Science and Technology · doi:10.1007/s11390-019-1929-5 · Cited by 62 · see citing works
Fine-to-Coarse Global Registration of RGB-D Scans
Halber, M., & Funkhouser, T. A. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.705 · Cited by 59 · see citing works
Hu, S., Cai, J.-X., & Lai, Y. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2889944 · Cited by 57 · see citing works
Lopes, A., de Souza, R. M., & Pedrini, H. (2022) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2022.103489 · Cited by 52 · see citing works
An Object SLAM Framework for Association, Mapping, and High-Level Tasks
Wu, Y., Zhang, Y., Zhu, D., Deng, Z., Sun, W., Chen, X., & Zhang, J. (2023) · IEEE Transactions on Robotics · doi:10.1109/tro.2023.3273180 · Cited by 52 · see citing works
Sung, J., Jin, S. H., & Saxena, A. (2018) · Springer Proceedings in Advanced Robotics · doi:10.1007/978-3-319-60916-4_40 · Cited by 52 · see citing works
“There has been great advancement in scene understanding [31, 41, 26, 61], in human activity detection [51, 19], and in features for RGB-D images and point-clouds [47, 29].”
BackgroundDROW: Real-Time Deep Learning-Based Wheelchair Detection in 2-D Range Data
Beyer, L., Hermans, A., & Leibe, B. (2016) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2016.2645131 · Cited by 51 · see citing works
Know What Your Neighbors Do: 3D Semantic Segmentation of Point Clouds
Engelmann, F., Kontogianni, T., Schult, J., & Leibe, B. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11015-4_29 · Cited by 50 · see citing works
NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D ScenesPreprint
Vora, S., Radwan, N., Greff, K., Meyer, H., Genova, K., Sajjadi, M. S. M., Pot, E., Tagliasacchi, A., & Duckworth, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.13260 · Cited by 50 · see citing works
Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters
Huang, S., & Ramanan, D. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.496 · Cited by 49 · see citing works
Lidar Mapping Optimization Based on Lightweight Semantic Segmentation
Zhao, Z., Zhang, W., Gu, J., Yang, J., & Huang, K. (2019) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2019.2919432 · Cited by 49 · see citing works
A Method for LSTM-Based Trajectory Modeling and Abnormal Trajectory Detection
Ji, Y., Wang, L., Wu, W., Shao, H., & Feng, Y. (2020) · IEEE Access · doi:10.1109/access.2020.2997967 · Cited by 49 · see citing works
Ersen, M., Öztop, E., & Sarıel, S. (2017) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2016.2616538 · Cited by 47 · see citing works
FoldingNet: Interpretable Unsupervised Learning on 3D Point CloudsPreprint
Yang, Y. L., Feng, C., Shen, Y., & Tian, D. (2017) · arXiv (Cornell University) · Cited by 47 · see citing works
Spherical Conformal Parameterization of Genus-0 Point Clouds for Meshing
Choi, G. P. T., Ho, K. T., & Lui, L. M. (2016) · SIAM Journal on Imaging Sciences · doi:10.1137/15m1037561 · Cited by 45 · see citing works
Learning Gaze Transitions from Depth to Improve Video Saliency Estimation
Leifman, G., Rudoy, D. V., Swedish, T. B., Bayro-Corrochano, E. J., & Raskar, R. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.188 · Cited by 45 · see citing works
GFOICP: Geometric Feature Optimized Iterative Closest Point for 3-D Point Cloud Registration
He, L., Wang, S., Hu, Q., Cai, Q., Li, M., Bai, Y., Wu, K., & Xiang, B. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3317822 · Cited by 43 · see citing works
Multimodal learning and inference from visual and remotely sensed data
Rao, D., Deuge, M. D., Nourani‐Vatani, N., Williams, S. B., & Pizarro, O. R. (2016) · The International Journal of Robotics Research · doi:10.1177/0278364916679892 · Cited by 43 · see citing works
Data‐Driven Shape Analysis and Processing
Xu, K., Kim, V. G., Qi-xing Huang, A., & Kalogerakis, E. (2016) · Computer Graphics Forum · doi:10.1111/cgf.12790 · Cited by 46 · see citing works
Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator for Static Video Surveillance
Hattori, H., Lee, N., Boddeti, V. N., Beainy, F., Kitani, K., & Takeo Kanade, A. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-018-1077-3 · Cited by 41 · see citing works
Planar Shape Detection at Structural Scales
Fang, H., Lafarge, F., & Desbrun, M. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00313 · Cited by 40 · see citing works
Robot-Centric Perception of Human Groups
Taylor, A. M., Chan, D. M., & Riek, L. D. (2020) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3375798 · Cited by 39 · see citing works
Learning an Effective Equivariant 3D Descriptor Without Supervision
Spezialetti, R., Salti, S., & Stefano, L. D. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00650 · Cited by 38 · see citing works
Li, M., Qin, Z., Gao, Z., Yi, R., Zhu, C., Guo, Y., & Xu, K. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01299 · Cited by 36 · see citing works
Learning Traversability From Point Clouds in Challenging Scenarios
Bellone, M., Reina, G., Caltagirone, L., & Wahde, M. (2017) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2769218 · Cited by 36 · see citing works
When Deep Learning Meets Data Alignment: A Review on Deep Registration Networks (DRNs)
Villena-Martínez, V., Oprea, S., Saval-Calvo, M., Azorín-López, J., Fuster-Guilló, A., & Fisher, R. B. (2020) · Applied Sciences · doi:10.3390/app10217524 · Cited by 35 · see citing works
Applying Domain Randomization to Synthetic Data for Object Category DetectionPreprint
Borrego, J., Dehban, A., de Figueiredo, R. P., Moreno, P., Bernardino, A. J. M., & Santos-Victor, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.09834 · Cited by 35 · see citing works
Recognizing Objects in-the-Wild: Where do we Stand?
Loghmani, M. R., Barbara Caputo, B., & Vincze, M. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8460985 · Cited by 35 · see citing works
MC-CIM: Compute-in-Memory With Monte-Carlo Dropouts for Bayesian Edge Intelligence
Shukla, P., Nasrin, S., Darabi, N., Gomes, W., & Trivedi, A. R. (2022) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2022.3224703 · Cited by 34 · see citing works
Fast and accurate ground plane detection for the visually impaired from 3D organized point clouds
Zeineldin, R. A., & El-Fishawy, N. A. (2016) · 2016 SAI Computing Conference (SAI) · doi:10.1109/sai.2016.7556009 · Cited by 33 · see citing works
Learning to Grasp Familiar Objects Based on Experience and Objects’ Shape Affordance
Liu, C., Fang, B., Sun, F., Li, X., & Huang, W. (2019) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2019.2901955 · Cited by 33 · see citing works
P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection From Point Clouds
Li, J., Sun, Y., Luo, S., Zhu, Z., Dai, H., Krylov, A. S., Ding, Y. S., & Shao, L. (2021) · IEEE Access · doi:10.1109/access.2021.3094562 · Cited by 34 · see citing works
Bousquet-Jetté, C., Achiche, S., Beaini, D., Cio, Y.-S. L.-K., Leblond-Ménard, C., & Raison, M. (2017) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2017.04.015 · Cited by 31 · see citing works
Semantic scene completion with dense CRF from a single depth image
Zhang, L., Wang, L., Zhang, X., Shen, P., Bennamoun, M., Zhu, G., Shah, S. A. A., & Song, J. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.08.052 · Cited by 31 · see citing works
ViDRILO: The Visual and Depth Robot Indoor Localization with Objects information dataset
Martínez-Gómez, J., García-Varea, I., Cazorla, M., & Morell, V. (2015) · The International Journal of Robotics Research · doi:10.1177/0278364915596058 · Cited by 30 · see citing works
- Authors
- Lai, K., Bo, L., & Fox, D.
- Affiliations
- University of Washington (US)
- Amazon (United States) (US)
- Venue
- 2014 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2014-05
- Pages
- 3050-3057
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2073700113
- Cited by
- 317
- References
- 41
- Citations by year
- 2026: 11 · 2025: 12 · 2024: 13 · 2023: 20 · 2022: 16 · 2021: 34 · 2020: 26 · 2019: 33 · 2018: 40 · 2017: 43 · 2016: 44 · 2015: 21 · 2014: 4
- FWCI
- 70.33
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- unsupervised feature learning, 3D point clouds, RGB-D, virtual scene generation, CAD models, indoor scene understanding
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
