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Although 3D Gaussian Splatting has been widely studied because of its realistic and efficient novel-view synthesis, it is still challenging to extract a high-quality surface from the point-based representation.
Poxels: Probabilistic voxelized volume reconstruction
Jeremy S De Bonet and Paul Viola · 1999
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Steven M Seitz and Charles R Dyer · 1999
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A theory of shape by space carving
Kiriakos N Kutulakos and Steven M Seitz · 2000
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A probabilistic framework for space carving
Adrian Broadhurst, Tom W Drummond, and Roberto Cipolla · 2001
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Surface splatting
Matthias Zwicker, Hanspeter Pfister, Jeroen Van Baar, and Markus Gross · 2001
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Perspective accurate splatting
Matthias Zwicker, Jussi Rasanen, Mario Botsch, Carsten Dachsbacher, and Mark Pauly · 2004
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A comparison and evaluation of multi-view stereo reconstruction algorithms
Steven M Seitz, Brian Curless, James Diebel, Daniel Scharstein, and Richard Szeliski · 2006
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Patchmatch stereo-stereo matching with slanted support windows
Michael Bleyer, Christoph Rhemann, and Carsten Rother · 2011
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Large scale multi-view stereopsis evaluation
Rasmus Jensen, Anders Dahl, George Vogiatzis, Engil Tola, and Henrik Aanæs · 2014
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Learning to compare image patches via convolutional neural networks
Sergey Zagoruyko and Nikos Komodakis · 2015
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Efficient deep learning for stereo matching
Wenjie Luo, Alexander G Schwing, and Raquel Urtasun · 2016
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Pixelwise view selection for unstructured multi-view stereo
Johannes L Schönberger, Enliang Zheng, Jan-Michael Frahm, and Marc Pollefeys · 2016
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Structure-from-motion revisited
Johannes Lutz Schönberger and Jan-Michael Frahm · 2016
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Tanks and temples: Benchmarking large-scale scene reconstruction
Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun · 2017
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Soft 3d reconstruction for view synthesis
Eric Penner and Li Zhang · 2017
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Octnetfusion: Learning depth fusion from data
Gernot Riegler, Ali Osman Ulusoy, Horst Bischof, and Andreas Geiger · 2017
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Multi-view supervision for single-view reconstruction via differentiable ray consistency
Shubham Tulsiani, Tinghui Zhou, Alexei A Efros, and Jitendra Malik · 2017
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Demon: Depth and motion network for learning monocular stereo
Benjamin Ummenhofer, Huizhong Zhou, Jonas Uhrig, Nikolaus Mayer, Eddy Ilg, Alexey Dosovitskiy, and Thomas Brox · 2017
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Deepmvs: Learning multi-view stereopsis
Po-Han Huang, Kevin Matzen, Johannes Kopf, Narendra Ahuja, and Jia-Bin Huang · 2018
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Mvsnet: Depth inference for unstructured multi-view stereo
Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan · 2018
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Open3D: A modern library for 3D data processing
Qian-Yi Zhou, Jaesik Park, and Vladlen Koltun · 2018
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Escaping plato’s cave: 3d shape from adversarial rendering
Philipp Henzler, Niloy J Mitra, and Tobias Ritschel · 2019
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Deepvoxels: Learning persistent 3d feature embeddings
Vincent Sitzmann, Justus Thies, Felix Heide, Matthias Nießner, Gordon Wetzstein, and Michael Zollhofer · 2019
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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
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Recurrent mvsnet for high-resolution multi-view stereo depth inference
Yao Yao, Zixin Luo, Shiwei Li, Tianwei Shen, Tian Fang, and Long Quan · 2019
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Differentiable surface splatting for point-based geometry processing
Wang Yifan, Felice Serena, Shihao Wu, Cengiz Öztireli, and Olga Sorkine-Hornung · 2019
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Enforcing geometric constraints of virtual normal for depth prediction
Wei Yin, Yifan Liu, Chunhua Shen, and Youliang Yan · 2019
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Instant neural graphics primitives with a multiresolution hash encoding
Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller · 2022
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Monosdf: Exploring monocular geometric cues for neural implicit surface reconstruction
Zehao Yu, Songyou Peng, Michael Niemeyer, Torsten Sattler, and Andreas Geiger · 2022
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Gnesf: Generalizable neural semantic fields
Hanlin Chen, Chen Li, Mengqi Guo, Zhiwen Yan, and Gim Hee Lee · 2023
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Neusg: Neural implicit surface reconstruction with 3d gaussian splatting guidance
Hanlin Chen, Chen Li, and Gim Hee Lee · 2023
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Lightgaussian: Unbounded 3d gaussian compression with 15x reduction and 200+ fps, 2023
Zhiwen Fan, Kevin Wang, Kairun Wen, Zehao Zhu, Dejia Xu, and Zhangyang Wang · 2023
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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
Ben Mildenhall, Pratul Srinivasan, Matthew Tancik, Jonathan Barron, Ravi Ramamoorthi, and Ren Ng · 2020
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Fast-mvsnet: Sparse-to-dense multi-view stereo with learned propagation and gauss-newton refinement
Zehao Yu and Shenghua Gao · 2020
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Visibility-aware multi-view stereo network
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, and Tian Fang · 2020
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Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields
Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan · 2021
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Fastnerf: High-fidelity neural rendering at 200fps
Stephan J Garbin, Marek Kowalski, Matthew Johnson, Jamie Shotton, and Julien Valentin · 2021
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Baking neural radiance fields for real-time view synthesis
Peter Hedman, Pratul P Srinivasan, Ben Mildenhall, Jonathan T Barron, and Paul Debevec · 2021
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3d gaussian splatting for real-time radiance field rendering
Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, and George Drettakis · 2023
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Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick · 2023
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Neuralangelo: High-fidelity neural surface reconstruction
Zhaoshuo Li, Thomas Müller, Alex Evans, Russell H Taylor, Mathias Unberath, Ming-Yu Liu, and Chen-Hsuan Lin · 2023
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Grounding dino: Marrying dino with grounded pre-training for open-set object detection
Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, et al · 2023
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Dreamgaussian: Generative gaussian splatting for efficient 3d content creation
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Gaussian grouping: Segment and edit anything in 3d scenes
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Rethinking inductive biases for surface normal estimation
Gwangbin Bae and Andrew J. Davison · 2024
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Geowizard: Unleashing the diffusion priors for 3d geometry estimation from a single image
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Sugar: Surface-aligned gaussian splatting for efficient 3d mesh reconstruction and high-quality mesh rendering
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Grounded sam: Assembling open-world models for diverse visual tasks, 2024
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Dn-splatter: Depth and normal priors for gaussian splatting and meshing, 2024
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Gov-nesf: Generalizable open-vocabulary neural semantic fields
Yunsong Wang, Hanlin Chen, and Gim Hee Lee · 2024
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