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In an effort to increase the capabilities of SLAM systems and produce object-level representations, the community increasingly investigates the imposition of higher-level priors into the estimation process.
Towards semantic slam using a monocular camera
J. Civera, D. Gálvez-López, L. Riazuelo, J. D. Tardós, and J. Montiel · 2011
Earlier work this paper cites.
Kinectfusion: Real-time dense surface mapping and tracking
R. A. Newcombe, S. Izadi, O. Hilliges, D. Molyneaux, D. Kim, A. J. Davison, P. Kohi, J. Shotton, S. Hodges, and A. Fitzgibbon · 2011
Earlier work this paper cites.
Dense visual SLAM for RGB-D cameras
C. Kerl, J. Sturm, and D. Cremers · 2013
Earlier work this paper cites.
Slam++: Simultaneous localisation and mapping at the level of objects
R. F. Salas-Moreno, R. A. Newcombe, H. Strasdat, P. H. Kelly, and A. J. Davison · 2013
Earlier work this paper cites.
Shapenet: An information-rich 3d model repository
A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, et al · 2015
Earlier work this paper cites.
Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
D. Eigen and R. Fergus · 2015
Earlier work this paper cites.
Single-view reconstruction via joint analysis of image and shape collections
Q. Huang, H. Wang, and V. Koltun · 2015
Earlier work this paper cites.
Elasticfusion: Dense slam without a pose graph
T. Whelan, S. Leutenegger, R. Salas-Moreno, B. Glocker, and A. Davison · 2015
Earlier work this paper cites.
3d shapenets: A deep representation for volumetric shapes
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao · 2015
Earlier work this paper cites.
Learning a predictable and generative vector representation for objects
R. Girdhar, D. F. Fouhey, M. Rodriguez, and A. Gupta · 2016
Earlier work this paper cites.
Slam with objects using a nonparametric pose graph
B. Mu, S.-Y. Liu, L. Paull, J. Leonard, and J. P. How · 2016
Earlier work this paper cites.
You only look once: Unified, real-time object detection
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi · 2016
Cited alongside, same era.
Perspective transformer nets: Learning single-view 3d object reconstruction without 3d supervision
X. Yan, J. Yang, E. Yumer, Y. Guo, and H. Lee · 2016
Cited alongside, same era.
Shape completion using 3d-encoder-predictor cnns and shape synthesis
A. Dai, C. R. Qi, and M. Nießner · 2017
Cited alongside, same era.
Hierarchical surface prediction for 3d object reconstruction
C. Häne, S. Tulsiani, and J. Malik · 2017
Cited alongside, same era.
Semanticfusion: Dense 3d semantic mapping with convolutional neural networks
J. McCormac, A. Handa, A. Davison, and S. Leutenegger · 2017
Cited alongside, same era.
Learning 3d object categories by looking around them
D. Novotny, D. Larlus, and A. Vedaldi · 2017
3d object reconstruction from a depth view with adversarial learning
B. Yang, H. Wen, S. Wang, R. Clark, A. Markham, and N. Trigoni · 2017
Later among the works it cites.
Semantic photometric bundle adjustment on natural sequences
R. Zhu, C. Wang, C.-H. Lin, Z. Wang, and S. Lucey · 2017
Later among the works it cites.
Scan2cad: Learning cad model alignment in rgb-d scans
A. Avetisyan, M. Dahnert, A. Dai, M. Savva, A. X. Chang, and M. Nießner · 2018
Later among the works it cites.
Incremental object database: Building 3d models from multiple partial observations
F. Furrer, T. Novkovic, M. Fehr, A. Gawel, M. Grinvald, T. Sattler, R. Siegwart, and J. Nieto · 2018
Later among the works it cites.
A papier-mâché approach to learning 3d surface generation
T. Groueix, M. Fisher, V. G. Kim, B. C. Russell, and M. Aubry · 2018
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Cited alongside, same era.
Octnet: Learning deep 3d representations at high resolutions
G. Riegler, A. Osman Ulusoy, and A. Geiger · 2017
Cited alongside, same era.
Octnetfusion: Learning depth fusion from data
G. Riegler, A. O. Ulusoy, H. Bischof, and A. Geiger · 2017
Cited alongside, same era.
Meaningful maps with object-oriented semantic mapping
N. Sünderhauf, T. T. Pham, Y. Latif, M. Milford, and I. Reid · 2017
Cited alongside, same era.
Octree generating networks: Efficient convolutional architectures for high-resolution 3d outputs
M. Tatarchenko, A. Dosovitskiy, and T. Brox · 2017
Cited alongside, same era.
Marrnet: 3d shape reconstruction via 2.5 d sketches
J. Wu, Y. Wang, T. Xue, X. Sun, B. Freeman, and J. Tenenbaum · 2017
Cited alongside, same era.
Later among the works it cites.
Dense object reconstruction from rgbd images with embedded deep shape representations
L. Hu, Y. Cao, P. Wu, and L. Kneip · 2018
Later among the works it cites.
Fusion++: Volumetric object-level slam
J. McCormac, R. Clark, M. Bloesch, A. Davison, and S. Leutenegger · 2018
Later among the works it cites.
Pix3d: Dataset and methods for single-image 3d shape modeling
X. Sun, J. Wu, X. Zhang, Z. Zhang, C. Zhang, T. Xue, J. B. Tenenbaum, and W. T. Freeman · 2018
Later among the works it cites.
Efficient object-oriented semantic mapping with object detector
Y. Nakajima and H. Saito · 2019
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Real-time progressive 3d semantic segmentation for indoor scenes
Q.-H. Pham, B.-S. Hua, T. Nguyen, and S.-K. Yeung · 2019
Closest in time.