Fetching the paper…
Reading the bibliography…
For the last few decades, several major subfields of artificial intelligence including computer vision, graphics, and robotics have progressed largely independently from each other.
L. G. Roberts, “Machine perception of three-dimensional solids,” Ph.D. dissertation, Massachusetts Institute of Technology, 1963
1963
Earlier work this paper cites.
P. H. Winston, “Heterarchy in the m.i.t. robot,” MIT Artificial Intelligence Laboratory, Tech. Rep., 1971
1971
Earlier work this paper cites.
S. Umeyama, “Least-squares estimation of transformation parameters between two point patterns,” PAMI , vol. 13, no. 4, pp. 376–380, 1991
1991
Earlier work this paper cites.
M. Ester, H. Kriegel, J. Sander, and X. Xu, “A density-based algorithm for discovering clusters in large spatial databases with noise,” in Proc. of the Second International Conference on Knowledge Discovery and Data Mining (KDD) , 1996
1996
Earlier work this paper cites.
F. Bernardini, J. Mittleman, H. Rushmeier, C. Silva, and G. Taubin, “The ball-pivoting algorithm for surface reconstruction,” VCG , vol. 5, no. 4, pp. 349–359, 1999
1999
Earlier work this paper cites.
S. M. Seitz and R. Szeliski, “Applications of computer vision to computer graphics,” in Computer Graphics , 1999
1999
Earlier work this paper cites.
Y. Boykov and V. Kolmogorov, “An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision.” PAMI , vol. 26, pp. 1124–1137, 2004
2004
Earlier work this paper cites.
H. Hirschmüller, “Stereo processing by semiglobal matching and mutual information,” PAMI , vol. 30, no. 2, pp. 328–341, 2008
2008
Earlier work this paper cites.
P. Osborne, “The mercator projections,” 2008. [Online]. Available: http://mercator.myzen.co.uk/mercator.pdf
2008
Earlier work this paper cites.
G. J. Brostow, J. Fauqueur, and R. Cipolla, “Semantic object classes in video: A high-definition ground truth database,” Pattern Recognition Letters , vol. 30, no. 2, pp. 88–97, 1 2009
2009
Earlier work this paper cites.
J. Xiao and L. Quan, “Multiple view semantic segmentation for street view images.” in ICCV , 2009
2009
Earlier work this paper cites.
V. Badrinarayanan, F. Galasso, and R. Cipolla, “Label propagation in video sequences,” in CVPR , 2010
2010
Earlier work this paper cites.
I. Budvytis, V. Badrinarayanan, and R. Cipolla, “Label propagation in complex video sequences using semi-supervised learning,” in BMVC , 2010
2010
Earlier work this paper cites.
N. Sundaram, T. Brox, and K. Keutzer, “Dense point trajectories by gpu-accelerated large displacement optical flow,” in ECCV , 2010
2010
Earlier work this paper cites.
M. Jancosek and T. Pajdla, “Multi-view reconstruction preserving weakly-supported surfaces,” in CVPR , 2011
2011
Earlier work this paper cites.
P. Krähenbühl and V. Koltun, “Efficient inference in fully connected CRFs with Gaussian edge potentials,” in NIPS , 2011
2011
Earlier work this paper cites.
C. Liu, J. Yuen, and A. Torralba, “Nonparametric scene parsing via label transfer,” PAMI , vol. 33, no. 12, pp. 2368–2382, 2011
2011
Earlier work this paper cites.
J. Behley, V. Steinhage, and A. B. Cremers, “Performance of histogram descriptors for the classification of 3d laser range data in urban environments,” in ICRA , 2012
2012
Earlier work this paper cites.
A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? The KITTI vision benchmark suite,” in CVPR , 2012
2012
Earlier work this paper cites.
D. Munoz, J. A. Bagnell, and M. Hebert, “Co-inference machines for multi-modal scene analysis,” in ECCV , 2012
2012
Earlier work this paper cites.
N. S. Nagaraja, P. Ochs, K. Liu, and T. Brox, “Hierarchy of localized random forests for video annotation,” in DAGM , 2012
2012
Earlier work this paper cites.
S. Vijayanarasimhan and K. Grauman, “Active frame selection for label propagation in videos,” in ECCV , 2012
2012
Earlier work this paper cites.
M. Zeiler, “Adadelta: An adaptive learning rate method,” arXiv.org , vol. 1212.5701, 2012
2012
Earlier work this paper cites.
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The KITTI dataset,” IJRR , vol. 32, no. 11, pp. 1231–1237, 2013
2013
Earlier work this paper cites.
L. Heng, B. Li, and M. Pollefeys, “Camodocal: Automatic intrinsic and extrinsic calibration of a rig with multiple generic cameras and odometry,” in IROS , 2013
2013
Earlier work this paper cites.
A. Hornung, K. M. Wurm, M. Bennewitz, C. Stachniss, and W. Burgard, “Octomap: An efficient probabilistic 3d mapping framework based on octrees,” in AR , vol. 34, no. 3. Springer, 2013, pp. 189–206
2013
Earlier work this paper cites.
J. P. Valentin, S. Sengupta, J. Warrell, A. Shahrokni, and P. H. Torr, “Mesh based semantic modelling for indoor and outdoor scenes,” in CVPR , 2013
2013
Earlier work this paper cites.
V. Vineet, G. Sheasby, J. Warrell, and P. H. S. Torr, “Posefield: An efficient mean-field based method for joint estimation of human pose, segmentation, and depth,” in EMMCVPR , 2013
2013
Earlier work this paper cites.
J. Xiao, A. Owens, and A. Torralba, “SUN3D: A database of big spaces reconstructed using sfm and object labels,” in ICCV , 2013
2013
Earlier work this paper cites.
H. Zhang, A. Geiger, and R. Urtasun, “Understanding high-level semantics by modeling traffic patterns,” in ICCV , 2013
2013
Earlier work this paper cites.
V. Badrinarayanan, I. Budvytis, and R. Cipolla, “Mixture of trees probabilistic graphical model for video segmentation,” IJCV , vol. 110, no. 1, pp. 14–29, 2014
2014
Earlier work this paper cites.
L.-C. Chen, S. Fidler, A. L. Yuille, and R. Urtasun, “Beat the mturkers: Automatic image labeling from weak 3d supervision,” in CVPR , 2014
2014
Earlier work this paper cites.
M. Guillaumin, D. Küttel, and V. Ferrari, “Imagenet auto-annotation with segmentation propagation,” IJCV , vol. 110, no. 3, pp. 328–348, 2014
2014
Earlier work this paper cites.
S. D. Jain and K. Grauman, “Supervoxel-consistent foreground propagation in video,” in ECCV , 2014
2014
Earlier work this paper cites.
M. Kiefel and P. Gehler, “Human pose estimation with fields of parts,” in ECCV , 2014
2014
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in ECCV , 2014
2014
Earlier work this paper cites.
H. Riemenschneider, A. Bódis-Szomorú, J. Weissenberg, and L. V. Gool, “Learning where to classify in multi-view semantic segmentation,” in ECCV , 2014
2014
Earlier work this paper cites.
M. Schönbein and A. Geiger, “Omnidirectional 3d reconstruction in augmented manhattan worlds,” in IROS , 2014
2014
Earlier work this paper cites.
M. Schönbein, T. Strauss, and A. Geiger, “Calibrating and centering quasi-central catadioptric cameras,” in ICRA , 2014
2014
Earlier work this paper cites.
A. Geiger and C. Wang, “Joint 3d object and layout inference from a single rgb-d image,” in GCPR , 2015
2015
Earlier work this paper cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in CVPR , 2015
2015
Cited alongside, same era.
A. Martinović, J. Knopp, H. Riemenschneider, and L. Van Gool, “3d all the way: Semantic segmentation of urban scenes from start to end in 3d,” in CVPR , 2015
2015
Cited alongside, same era.
S. T. Namin, M. Najafi, M. Salzmann, and L. Petersson, “A multi-modal graphical model for scene analysis,” in WACV , 2015
2015
Cited alongside, same era.
S. Song, S. Lichtenberg, and J. Xiao., “Sun rgb-d: A rgb-d scene understanding benchmark suite,” in CVPR , 2015
2015
Cited alongside, same era.
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The cityscapes dataset for semantic urban scene understanding,” in CVPR , 2016
2016
Cited alongside, same era.
M. Chang, J. Lambert, P. Sangkloy, J. Singh, S. Bak, A. Hartnett, D. Wang, P. Carr, S. Lucey, D. Ramanan, and J. Hays, “Argoverse: 3d tracking and forecasting with rich maps,” in CVPR , 2019
2019
Later among the works it cites.
Y. Chen, C. Dong, P. Palanisamy, P. Mudalige, K. Muelling, and J. M. Dolan, “Attention-based hierarchical deep reinforcement learning for lane change behaviors in autonomous driving,” in IROS , 2019
2019
Later among the works it cites.
W. Ding, L. Zhang, J. Chen, and S. Shen, “Safe trajectory generation for complex urban environments using spatio-temporal semantic corridor,” IEEE Robotics and Automation Letters , 2019
2019
Later among the works it cites.
R. Kesten, M. Usman, J. Houston, T. Pandya, K. Nadhamuni, A. Ferreira, M. Yuan, B. Low, A. Jain, P. Ondruska, S. Omari, S. Shah, A. Kulkarni, A. Kazakova, C. Tao, L. Platinsky, W. Jiang, and V. Shet, “Level 5 perception dataset 2020,” https://level-5.global/level5/data/ , 2019
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Gaidon, Q. Wang, Y. Cabon, and E. Vig, “Virtual worlds as proxy for multi-object tracking analysis,” in CVPR , 2016
2016
Cited alongside, same era.
J. Hoffman, D. Wang, F. Yu, and T. Darrell, “Fcns in the wild: Pixel-level adversarial and constraint-based adaptation,” arXiv.org , vol. 1612.02649, 2016
2016
Cited alongside, same era.
S. R. Richter, V. Vineet, S. Roth, and V. Koltun, “Playing for data: Ground truth from computer games,” in ECCV , 2016
2016
Cited alongside, same era.
G. Ros, L. Sellart, J. Materzynska, D. Vazquez, and A. Lopez, “The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes,” in CVPR , 2016
2016
Cited alongside, same era.
J. Xie, M. Kiefel, M.-T. Sun, and A. Geiger, “Semantic instance annotation of street scenes by 3d to 2d label transfer,” in CVPR , 2016
2016
Cited alongside, same era.
I. Budvytis, P. Sauer, T. Roddick, K. Breen, and R. Cipolla, “Large scale labelled video data augmentation for semantic segmentation in driving scenarios,” in ICCV , 2017
2017
Cited alongside, same era.
L. Castrejon, K. Kundu, R. Urtasun, and S. Fidler, “Annotating object instances with a polygon-rnn,” in CVPR , 2017
2017
Cited alongside, same era.
M. Larsson, E. Stenborg, L. Hammarstrand, M. Pollefeys, T. Sattler, and F. Kahl, “A cross-season correspondence dataset for robust semantic segmentation,” in CVPR , 2019
2019
Later among the works it cites.
H. Ling, J. Gao, A. Kar, W. Chen, and S. Fidler, “Fast interactive object annotation with curve-gcn,” in CVPR , 2019
2019
Later among the works it cites.
L. Nicholson, M. Milford, and N. Sünderhauf, “Quadricslam: Dual quadrics from object detections as landmarks in object-oriented SLAM,” IEEE Robotics and Automation Letters , 2019
2019
Later among the works it cites.
C. R. Qi, O. Litany, K. He, and L. J. Guibas, “Deep hough voting for 3d object detection in point clouds,” in ICCV , 2019
2019
Later among the works it cites.
Y. Xiong, R. Liao, H. Zhao, R. Hu, M. Bai, E. Yumer, and R. Urtasun, “Upsnet: A unified panoptic segmentation network,” in CVPR , 2019
2019
Later among the works it cites.
S. Yang and S. A. Scherer, “Cubeslam: Monocular 3-d object SLAM,” vol. 35, no. 4, pp. 925–938, 2019
2019
Later among the works it cites.
S. Yogamani, C. Hughes, J. Horgan, G. Sistu, P. Varley, D. O’Dea, M. Uricár, S. Milz, M. Simon, K. Amende, C. Witt, and H. Rashed, “Woodscape: A multi-task, multi-camera fisheye dataset for autonomous driving,” in ICCV , 2019
2019
Later among the works it cites.
Y. Zhu, K. Sapra, F. A. Reda, K. J. Shih, S. D. Newsam, A. Tao, and B. Catanzaro, “Improving semantic segmentation via video propagation and label relaxation,” in CVPR , 2019
2019
Later among the works it cites.
Y. Cabon, N. Murray, and M. Humenberger, “Virtual KITTI 2,” arXiv.org , vol. 2001.10773, 2020
2020
Later among the works it cites.
H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” in CVPR , 2020
2020
Later among the works it cites.
J. Geyer, Y. Kassahun, M. Mahmudi, X. Ricou, R. Durgesh, A. S. Chung, L. Hauswald, V. H. Pham, M. Mühlegg, S. Dorn, T. Fernandez, M. Jänicke, S. Mirashi, C. Savani, M. Sturm, O. Vorobiov, M. Oelker, S. Garreis, and P. Schuberth, “A2D2: audi autonomous driving dataset,” arXiv.org , vol. 2004.06320, 2020
2020
Later among the works it cites.
K. He, G. Gkioxari, P. Dollár, and R. B. Girshick, “Mask R-CNN,” PAMI , vol. 42, no. 2, pp. 386–397, 2020
2020
Later among the works it cites.
X. Huang, P. Wang, X. Cheng, D. Zhou, Q. Geng, and R. Yang, “The apolloscape open dataset for autonomous driving and its application,” PAMI , 2020
2020
Later among the works it cites.
J. Janai, F. Güney, A. Behl, and A. Geiger, “Computer vision for autonomous vehicles: Problems, datasets and state of the art,” Foundations and Trends in Computer Graphics and Vision , 2020
2020
Later among the works it cites.
L. Jiang, H. Zhao, S. Shi, S. Liu, C. Fu, and J. Jia, “Pointgroup: Dual-set point grouping for 3d instance segmentation,” in CVPR , 2020
2020
Later among the works it cites.
D. Kim, S. Woo, J.-Y. Lee, and I. S. Kweon, “Video panoptic segmentation,” in CVPR , 2020
2020
Later among the works it cites.
Y. Liao, K. Schwarz, L. Mescheder, and A. Geiger, “Towards unsupervised learning of generative models for 3d controllable image synthesis,” in CVPR , 2020
2020
Later among the works it cites.
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “NeRF: Representing scenes as neural radiance fields for view synthesis,” in ECCV , 2020
2020
Later among the works it cites.
Q.-H. Pham, P. Sevestre, R. S. Pahwa, H. Zhan, C. H. Pang, Y. Chen, A. Mustafa, V. Chandrasekhar, and J. Lin, “A*3d dataset: Towards autonomous driving in challenging environments,” in ICRA , 2020
2020
Later among the works it cites.
G. Riegler and V. Koltun, “Free view synthesis,” in ECCV , 2020
2020
Later among the works it cites.
A. Rosinol, M. Abate, Y. Chang, and L. Carlone, “Kimera: an open-source library for real-time metric-semantic localization and mapping,” in ICRA , 2020
2020
Later among the works it cites.
P. Sun, H. Kretzschmar, X. Dotiwalla, A. Chouard, V. Patnaik, P. Tsui, J. Guo, Y. Zhou, Y. Chai, B. Caine, V. Vasudevan, W. Han, J. Ngiam, H. Zhao, A. Timofeev, S. Ettinger, M. Krivokon, A. Gao, A. Joshi, Y. Zhang, J. Shlens, Z. Chen, and D. Anguelov, “Scalability in perception for autonomous driving: Waymo open dataset,” in CVPR , 2020
2020
Later among the works it cites.
W. Tan, N. Qin, L. Ma, Y. Li, J. Du, G. Cai, K. Yang, and J. Li, “Toronto-3d: A large-scale mobile lidar dataset for semantic segmentation of urban roadways,” in CVPR Workshops , 2020
2020
Later among the works it cites.
Z. Yang, Y. Chai, D. Anguelov, Y. Zhou, P. Sun, D. Erhan, S. Rafferty, and H. Kretzschmar, “Surfelgan: Synthesizing realistic sensor data for autonomous driving,” in CVPR , 2020
2020
Later among the works it cites.
S. Zakharov, W. Kehl, A. Bhargava, and A. Gaidon, “Autolabeling 3d objects with differentiable rendering of SDF shape priors,” in CVPR , 2020
2020
Later among the works it cites.
M. Aygun, A. Osep, M. Weber, M. Maximov, C. Stachniss, J. Behley, and L. Leal-Taixe, “4d panoptic lidar segmentation,” in CVPR , 2021
2021
Closest in time.
J. T. Barron, B. Mildenhall, M. Tancik, P. Hedman, R. Martin-Brualla, and P. P. Srinivasan, “Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields,” in ICCV , 2021
2021
Closest in time.
Y. Chen, F. Rong, S. Duggal, S. Wang, X. Yan, S. Manivasagam, S. Xue, E. Yumer, and R. Urtasun, “Geosim: Realistic video simulation via geometry-aware composition for self-driving,” in CVPR , 2021
2021
Closest in time.
K. Deng, A. Liu, J. Zhu, and D. Ramanan, “Depth-supervised nerf: Fewer views and faster training for free,” arXiv.org , vol. 2107.02791, 2021
2021
Closest in time.
G. Kopanas, J. Philip, T. Leimkühler, and G. Drettakis, “Point-based neural rendering with per-view optimization,” Computer Graphics Forum , vol. 40, no. 4, pp. 29–43, 2021
2021
Closest in time.
M. Niemeyer and A. Geiger, “Giraffe: Representing scenes as compositional generative neural feature fields,” in CVPR , 2021
2021
Closest in time.
J. Ost, F. Mannan, N. Thuerey, J. Knodt, and F. Heide, “Neural scene graphs for dynamic scenes,” CVPR , 2021
2021
Closest in time.
C. R. Qi, Y. Zhou, M. Najibi, P. Sun, K. Vo, B. Deng, and D. Anguelov, “Offboard 3d object detection from point cloud sequences,” in CVPR , 2021
2021
Closest in time.
S. Qiao, Y. Zhu, H. Adam, A. L. Yuille, and L. Chen, “Vip-deeplab: Learning visual perception with depth-aware video panoptic segmentation,” in CVPR , 2021
2021
Closest in time.
M. Weber, J. Xie, M. Collins, Y. Zhu, P. Voigtlaender, H. Adam, B. Green, A. Geiger, B. Leibe, D. Cremers, A. Osep, L. Leal-Taixe, and L.-C. Chen, “STEP: Segmenting and tracking every pixel,” in NeurIPS Datasets and Benchmarks , 2021
2021
Closest in time.