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In recent years, coordinate-based neural implicit representations have shown promising results for the task of Simultaneous Localization and Mapping (SLAM).
Monoslam: Real-time single camera slam
Andrew J Davison, Ian D Reid, Nicholas D Molton, and Olivier Stasse · 2007
Earlier work this paper cites.
Parallel tracking and mapping for small ar workspaces
Georg Klein and David Murray · 2007
Earlier work this paper cites.
Kinectfusion: real-time 3d reconstruction and interaction using a moving depth camera
Shahram Izadi, David Kim, Otmar Hilliges, David Molyneaux, Richard Newcombe, Pushmeet Kohli, Jamie Shotton, Steve Hodges, Dustin Freeman, Andrew Davison, et al · 2011
Earlier work this paper cites.
Dtam: Dense tracking and mapping in real-time
Richard A Newcombe, Steven J Lovegrove, and Andrew J Davison · 2011
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A benchmark for the evaluation of rgb-d slam systems
Jürgen Sturm, Nikolas Engelhard, Felix Endres, Wolfram Burgard, and Daniel Cremers · 2012
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Real-time 3d reconstruction at scale using voxel hashing
Matthias Nießner, Michael Zollhöfer, Shahram Izadi, and Marc Stamminger · 2013
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Hierarchical voxel block hashing for efficient integration of depth images
Olaf Kähler, Victor Prisacariu, Julien Valentin, and David Murray · 2015
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Orb-slam: a versatile and accurate monocular slam system
Raul Mur-Artal, Jose Maria Martinez Montiel, and Juan D Tardos · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Elasticfusion: Dense slam without a pose graph
Thomas Whelan, Stefan Leutenegger, Renato Salas-Moreno, Ben Glocker, and Andrew Davison · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Codeslam—learning a compact, optimisable representation for dense visual slam
Michael Bloesch, Jan Czarnowski, Ronald Clark, Stefan Leutenegger, and Andrew J Davison · 2018
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Deepv2d: Video to depth with differentiable structure from motion
Zachary Teed and Jia Deng · 2018
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Efficient octree-based volumetric slam supporting signed-distance and occupancy mapping
Emanuele Vespa, Nikolay Nikolov, Marius Grimm, Luigi Nardi, Paul HJ Kelly, and Stefan Leutenegger · 2018
Earlier work this paper cites.
The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
Earlier work this paper cites.
Deeptam: Deep tracking and mapping
Huizhong Zhou, Benjamin Ummenhofer, and Thomas Brox · 2018
Earlier work this paper cites.
Learning implicit fields for generative shape modeling
Zhiqin Chen and Hao Zhang · 2019
Earlier work this paper cites.
Occupancy networks: Learning 3d reconstruction in function space
Lars M. Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger · 2019
Cited alongside, same era.
Neural importance sampling
Thomas Müller, Brian McWilliams, Fabrice Rousselle, Markus Gross, and Jan Novák · 2019
Cited alongside, same era.
Deepsdf: Learning continuous signed distance functions for shape representation
Jeong Joon Park, Peter R. Florence, Julian Straub, Richard A. Newcombe, and Steven Lovegrove · 2019
Cited alongside, same era.
Bad slam: Bundle adjusted direct rgb-d slam
Thomas Schops, Torsten Sattler, and Marc Pollefeys · 2019
Cited alongside, same era.
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, et al · 2019
Cited alongside, same era.
pixelnerf: Neural radiance fields from one or few images
Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa · 2021
Later among the works it cites.
In-place scene labelling and understanding with implicit scene representation
Shuaifeng Zhi, Tristan Laidlow, Stefan Leutenegger, and Andrew J Davison · 2021
Later among the works it cites.
Neural rgb-d surface reconstruction
Dejan Azinović, Ricardo Martin-Brualla, Dan B Goldman, Matthias Nießner, and Justus Thies · 2022
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Neural 3d scene reconstruction with the manhattan-world assumption
Haoyu Guo, Sida Peng, Haotong Lin, Qianqian Wang, Guofeng Zhang, Hujun Bao, and Xiaowei Zhou · 2022
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Panoptic neural fields: A semantic object-aware neural scene representation
Abhijit Kundu, Kyle Genova, Xiaoqi Yin, Alireza Fathi, Caroline Pantofaru, Leonidas J Guibas, Andrea Tagliasacchi, Frank Dellaert, and Thomas Funkhouser · 2022
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Scenecode: Monocular dense semantic reconstruction using learned encoded scene representations
Shuaifeng Zhi, Michael Bloesch, Stefan Leutenegger, and Andrew J Davison · 2019
Cited alongside, same era.
Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision
Michael Niemeyer, Lars M. Mescheder, Michael Oechsle, and Andreas Geiger · 2020
Cited alongside, same era.
Nodeslam: Neural object descriptors for multi-view shape reconstruction
Edgar Sucar, Kentaro Wada, and Andrew Davison · 2020
Cited alongside, same era.
Routedfusion: Learning real-time depth map fusion
Silvan Weder, Johannes Schonberger, Marc Pollefeys, and Martin R Oswald · 2020
Cited alongside, same era.
D3vo: Deep depth, deep pose and deep uncertainty for monocular visual odometry
Nan Yang, Lukas von Stumberg, Rui Wang, and Daniel Cremers · 2020
Cited alongside, same era.
Di-fusion: Online implicit 3d reconstruction with deep priors
Jiahui Huang, Shi-Sheng Huang, Haoxuan Song, and Shi-Min Hu · 2021
Cited alongside, same era.
Nerf: Representing scenes as neural radiance fields for view synthesis
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng · 2021
Cited alongside, same era.
Ben Mildenhall, Peter Hedman, Ricardo Martin-Brualla, Pratul P Srinivasan, and Jonathan T Barron · 2022
Later among the works it cites.
Instant neural graphics primitives with a multiresolution hash encoding
Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller · 2022
Later among the works it cites.
Monosdf: Exploring monocular geometric cues for neural implicit surface reconstruction
Zehao Yu, Songyou Peng, Michael Niemeyer, Torsten Sattler, and Andreas Geiger · 2022
Later among the works it cites.
Detecting twenty-thousand classes using image-level supervision
Xingyi Zhou, Rohit Girdhar, Armand Joulin, Philipp Krähenbühl, and Ishan Misra · 2022
Later among the works it cites.
Nice-slam: Neural implicit scalable encoding for slam
Zihan Zhu, Songyou Peng, Viktor Larsson, Weiwei Xu, Hujun Bao, Zhaopeng Cui, Martin R Oswald, and Marc Pollefeys · 2022
Later among the works it cites.
Nope-nerf: Optimising neural radiance field with no pose prior
Wenjing Bian, Zirui Wang, Kejie Li, Jia-Wang Bian, and Victor Adrian Prisacariu · 2023
Closest in time.
Neural implicit dense semantic slam
Yasaman Haghighi, Suryansh Kumar, Jean Philippe Thiran, and Luc Van Gool · 2023
Closest in time.
Learning neural implicit through volume rendering with attentive depth fusion priors
Pengchong Hu and Zhizhong Han · 2023
Closest in time.
Eslam: Efficient dense slam system based on hybrid representation of signed distance fields
Mohammad Mahdi Johari, Camilla Carta, and François Fleuret · 2023
Closest in time.
vmap: Vectorised object mapping for neural field slam
Xin Kong, Shikun Liu, Marwan Taher, and Andrew J Davison · 2023
Closest in time.
Point-slam: Dense neural point cloud-based slam
Erik Sandström, Yue Li, Luc Van Gool, and Martin R Oswald · 2023
Closest in time.
Co-slam: Joint coordinate and sparse parametric encodings for neural real-time slam
Hengyi Wang, Jingwen Wang, and Lourdes Agapito · 2023
Closest in time.