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Semantic labelling is highly correlated with geometry and radiance reconstruction, as scene entities with similar shape and appearance are more likely to come from similar classes.
Indoor segmentation and support inference from RGBD images
N. Silberman, D. Hoiem, P. Kohli, and R. Fergus · 2012
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Dense 3D semantic mapping of indoor scenes from RGB-D images
Alexander Hermans, Georgios Floros, and Bastian Leibe · 2014
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Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture
David Eigen and Rob Fergus · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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SUN RGB-D: A RGB-D scene understanding benchmark suite
Shuran Song, Samuel P Lichtenberg, and Jianxiong Xiao · 2015
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ScanNet: Richly-annotated 3d reconstructions of indoor scene
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner · 2017
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SemanticFusion: Dense 3D semantic mapping with convolutional neural networks
J. McCormac, A. Handa, A. J. Davison, and S. Leutenegger · 2017
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SceneNet RGB-D: Can 5M synthetic images beat generic ImageNet pre-training on indoor segmentation?
J. McCormac, A. Handa, S. Leutenegger, and A. J. Davison · 2017
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Meaningful maps with object-oriented semantic mapping
N. Sünderhauf, T. T. Pham, Y. Latif, M. Milford, and I. Reid · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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CodeSLAM — learning a compact, optimisable representation for dense visual SLAM
M. Bloesch, J. Czarnowski, R. Clark, S. Leutenegger, and A. J. Davison · 2018
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Encoder-decoder with atrous separable convolution for semantic image segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam · 2018
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Neural scene representation and rendering
SM Ali Eslami, Danilo Jimenez Rezende, Frederic Besse, Fabio Viola, Ari S Morcos, Marta Garnelo, Avraham Ruderman, Andrei A Rusu, Ivo Danihelka, Karol Gregor, et al · 2018
Cited alongside, same era.
Fast and accurate semantic mapping through geometric-based incremental segmentation
Yoshikatsu Nakajima, Keisuke Tateno, Federico Tombari, and Hideo Saito · 2018
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Maskfusion: Real-time Recognition, Tracking and Reconstruction of Multiple Moving Objects
Martin Runz, Maud Buffier, and Lourdes Agapito · 2018
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Taskonomy: Disentangling task transfer learning
A. R. Zamir, A. Sax, W. B. Shen, L. J. Guibas, J. Malik, and S. Savarese · 2018
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End-to-end multi-task learning with attention
Shikun Liu, Edward Johns, and Andrew J Davison · 2019
Cited alongside, same era.
Occupancy networks: Learning 3d reconstruction in function space
The Replica dataset: A digital replica of indoor spaces
Julian Straub, Thomas Whelan, Lingni Ma, Yufan Chen, Erik Wijmans, Simon Green, Jakob J. Engel, Raul Mur-Artal, Carl Ren, Shobhit Verma, Anton Clarkson, Mingfei Yan, Brian Budge, Yajie Yan, Xiaqing Pan, June Yon, Yuyang Zou, Kimberly Leon, Nigel Carter, Jesus Briales, Tyler Gillingham, Elias Mueggler, Luis Pesqueira, Manolis Savva, Dhruv Batra, Hauke M. Strasdat, Renzo De Nardi, Michael Goesele, Steven Lovegrove, and Richard Newcombe · 2019
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SceneCode: Monocular dense semantic reconstruction using learned encoded scene representations
S. Zhi, M. Bloesch, S. Leutenegger, and A. J. Davison · 2019
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Inferring semantic information with 3d neural scene representations
Amit Kohli, Vincent Sitzmann, and Gordon Wetzstein · 2020
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DIST: Rendering deep implicit signed distance function with differentiable sphere tracing
Shaohui Liu, Yinda Zhang, Songyou Peng, Boxin Shi, Marc Pollefeys, and Zhaopeng Cui · 2020
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NeRF: Representing scenes as neural radiance fields for view synthesis
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Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger · 2019
Cited alongside, same era.
Panopticfusion: Online volumetric semantic mapping at the level of stuff and things
Gaku Narita, Takashi Seno, Tomoya Ishikawa, and Yohsuke Kaji · 2019
Cited alongside, same era.
Deepsdf: Learning continuous signed distance functions for shape representation
Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Cited alongside, same era.
Habitat: A platform for embodied ai research
Manolis Savva, Abhishek Kadian, Oleksandr Maksymets, Yili Zhao, Erik Wijmans, Bhavana Jain, Julian Straub, Jia Liu, Vladlen Koltun, Jitendra Malik, et al · 2019
Cited alongside, same era.
Scene representation networks: Continuous 3d-structure-aware neural scene representations
Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein · 2019
Cited alongside, same era.
Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng · 2020
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Convolutional occupancy networks
Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger · 2020
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Fourier features let networks learn high frequency functions in low dimensional domains
Matthew Tancik, Pratul P. Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T. Barron, and Ren Ng · 2020
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GRF: Learning a general radiance field for 3d scene representation and rendering
Alex Trevithick and Bo Yang · 2020
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Nerf++: Analyzing and improving neural radiance fields
Kai Zhang, Gernot Riegler, Noah Snavely, and Vladlen Koltun · 2020
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Nerf in the wild: Neural radiance fields for unconstrained photo collections
Ricardo Martin-Brualla, Noha Radwan, Mehdi SM Sajjadi, Jonathan T Barron, Alexey Dosovitskiy, and Daniel Duckworth · 2021
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Nerv: Neural reflectance and visibility fields for relighting and view synthesis
Pratul P Srinivasan, Boyang Deng, Xiuming Zhang, Matthew Tancik, Ben Mildenhall, and Jonathan T Barron · 2021
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