Fetching the paper…
Reading the bibliography…
Current methods for 3D scene reconstruction from sparse posed images employ intermediate 3D representations such as neural fields, voxel grids, or 3D Gaussians, to achieve multi-view consistent scene appearance and geometry.
A benchmark for the evaluation of rgb-d slam systems
J. Sturm, N. Engelhard, F. Endres, W. Burgard, and D. Cremers · 2012
Earlier work this paper cites.
Sun3d: A database of big spaces reconstructed using sfm and object labels
Jianxiong Xiao, Andrew Owens, and Antonio Torralba · 2013
Earlier work this paper cites.
Large scale multi-view stereopsis evaluation
Rasmus Jensen, Anders Dahl, George Vogiatzis, Engil Tola, and Henrik Aanæs · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Earlier work this paper cites.
Structure-from-motion revisited
Johannes L Schonberger and Jan-Michael Frahm · 2016
Earlier work this paper cites.
Scannet: Richly-annotated 3d reconstructions of indoor scenes
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Neural discrete representation learning
Aaron van den Oord, Oriol Vinyals, and koray kavukcuoglu · 2017
Earlier work this paper cites.
Accurate, large minibatch sgd: Training imagenet in 1 hour, 2018
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 2018
Earlier work this paper cites.
BA-Net: Dense bundle adjustment network
Chengzhou Tang and Ping Tan · 2018
Earlier work this paper cites.
MVSNet: Depth inference for unstructured multi-view stereo
Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan · 2018
Earlier work this paper cites.
Taskonomy: Disentangling task transfer learning
Amir R Zamir, Alexander Sax, , William B Shen, Leonidas Guibas, Jitendra Malik, and Silvio Savarese · 2018
Earlier work this paper cites.
Stereo magnification: Learning view synthesis using multiplane images
Tinghui Zhou, Richard Tucker, John Flynn, Graham Fyffe, and Noah Snavely · 2018
Earlier work this paper cites.
Digging into self-supervised monocular depth prediction
Clément Godard, Oisin Mac Aodha, Michael Firman, and Gabriel J. Brostow · 2019
Earlier work this paper cites.
Dpsnet: End-to-end deep plane sweep stereo
Sunghoon Im, Hae-Gon Jeon, Stephen Lin, and In So Kweon · 2019
Earlier work this paper cites.
Decoupled weight decay regularization, 2019
Ilya Loshchilov and Frank Hutter · 2019
Earlier work this paper cites.
Local light field fusion: Practical view synthesis with prescriptive sampling guidelines
Ben Mildenhall, Pratul P. Srinivasan, Rodrigo Ortiz-Cayon, Nima Khademi Kalantari, Ravi Ramamoorthi, Ren Ng, and Abhishek Kar · 2019
Earlier work this paper cites.
Yohann Cabon, Naila Murray, and Martin Humenberger · 2020
Earlier work this paper cites.
nuscenes: A multimodal dataset for autonomous driving
Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom · 2020
Earlier work this paper cites.
pi-gan: Periodic implicit generative adversarial networks for 3d-aware image synthesis
Eric Chan, Marco Monteiro, Petr Kellnhofer, Jiajun Wu, and Gordon Wetzstein · 2020
Earlier work this paper cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Earlier work this paper cites.
One thousand and one hours: Self-driving motion prediction dataset
John Houston, Guido Zuidhof, Luca Bergamini, Yawei Ye, Long Chen, Ashesh Jain, Sammy Omari, Vladimir Iglovikov, and Peter Ondruska · 2020
Earlier work this paper cites.
Deep multi-view depth estimation with predicted uncertainty
Tong Ke, Tien Do, Khiem Vuong, Kourosh Sartipi, and Stergios I Roumeliotis · 2020
Earlier work this paper cites.
Normal assisted stereo depth estimation
Uday Kusupati, Shuo Cheng, Rui Chen, and Hao Su · 2020
Earlier work this paper cites.
Consistent video depth estimation
Xuan Luo, Jia-Bin Huang, Richard Szeliski, Kevin Matzen, and Johannes Kopf · 2020
Earlier work this paper cites.
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 · 2020
Earlier work this paper cites.
Feature-metric loss for self-supervised learning of depth and egomotion
Chang Shu, Kun Yu, Zhixiang Duan, and Kuiyuan Yang · 2020
Earlier work this paper cites.
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
Earlier work this paper cites.
Scalability in perception for autonomous driving: Waymo open dataset
Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, et al · 2020
Earlier work this paper cites.
Deepv2d: Video to depth with differentiable structure from motion
Zachary Teed and Jia Deng · 2020
Earlier work this paper cites.
Tartanair: A dataset to push the limits of visual slam
Wenshan Wang, Delong Zhu, Xiangwei Wang, Yaoyu Hu, Yuheng Qiu, Chen Wang, Yafei Hu, Ashish Kapoor, and Sebastian Scherer · 2020
Earlier work this paper cites.
Blendedmvs: A large-scale dataset for generalized multi-view stereo networks
Yao Yao, Zixin Luo, Shiwei Li, Jingyang Zhang, Yufan Ren, Lei Zhou, Tian Fang, and Long Quan · 2020
Earlier work this paper cites.
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
Earlier work this paper cites.
Efficient geometry-aware 3D generative adversarial networks
Eric R. Chan, Connor Z. Lin, Matthew A. Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas Guibas, Jonathan Tremblay, Sameh Khamis, Tero Karras, and Gordon Wetzstein · 2021
Earlier work this paper cites.
Depth-supervised nerf: Fewer views and faster training for free
Kangle Deng, Andrew Liu, Jun-Yan Zhu, , and Deva Ramanan · 2021
Cited alongside, same era.
Unconstrained scene generation with locally conditioned radiance fields
Terrance DeVries, Miguel Angel Bautista, Nitish Srivastava, Graham W Taylor, and Joshua M Susskind · 2021
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Cited alongside, same era.
Omnidata: A scalable pipeline for making multi-task mid-level vision datasets from 3d scans
Ainaz Eftekhar, Alexander Sax, Jitendra Malik, and Amir Zamir · 2021
Cited alongside, same era.
Putting nerf on a diet: Semantically consistent few-shot view synthesis
Ajay Jain, Matthew Tancik, and Pieter Abbeel · 2021
Cited alongside, same era.
Midas v3.1 – a model zoo for robust monocular relative depth estimation, 2023
Reiner Birkl, Diana Wofk, and Matthias Müller · 2023
Later among the works it cites.
GeNVS: Generative novel view synthesis with 3D-aware diffusion models
Eric R. Chan, Koki Nagano, Matthew A. Chan, Alexander W. Bergman, Jeong Joon Park, Axel Levy, Miika Aittala, Shalini De Mello, Tero Karras, and Gordon Wetzstein · 2023
Later among the works it cites.
On the design fundamentals of diffusion models: A survey, 2023
Ziyi Chang, George Alex Koulieris, and Hubert P. H. Shum · 2023
Later among the works it cites.
pixelsplat: 3d gaussian splats from image pairs for scalable generalizable 3d reconstruction
David Charatan, Sizhe Li, Andrea Tagliasacchi, and Vincent Sitzmann · 2023
Later among the works it cites.
Learning to render novel views from wide-baseline stereo pairs
Yilun Du, Cameron Smith, Ayush Tewari, and Vincent Sitzmann · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Infinite nature: Perpetual view generation of natural scenes from a single image
Andrew Liu, Richard Tucker, Varun Jampani, Ameesh Makadia, Noah Snavely, and Angjoo Kanazawa · 2021
Cited alongside, same era.
Giraffe: Representing scenes as compositional generative neural feature fields
Michael Niemeyer and Andreas Geiger · 2021
Cited alongside, same era.
Habitat-matterport 3d dataset (HM3d): 1000 large-scale 3d environments for embodied AI
Santhosh Kumar Ramakrishnan, Aaron Gokaslan, Erik Wijmans, Oleksandr Maksymets, Alexander Clegg, John M Turner, Eric Undersander, Wojciech Galuba, Andrew Westbury, Angel X Chang, Manolis Savva, Yili Zhao, and Dhruv Batra · 2021
Cited alongside, same era.
Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
Cited alongside, same era.
Common objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction
Jeremy Reizenstein, Roman Shapovalov, Philipp Henzler, Luca Sbordone, Patrick Labatut, and David Novotny · 2021
Cited alongside, same era.
Hypersim: A photorealistic synthetic dataset for holistic indoor scene understanding
Mike Roberts, Jason Ramapuram, Anurag Ranjan, Atulit Kumar, Miguel Angel Bautista, Nathan Paczan, Russ Webb, and Joshua M. Susskind · 2021
Cited alongside, same era.
Light field networks: Neural scene representations with single-evaluation rendering
Vincent Sitzmann, Semon Rezchikov, William T. Freeman, Joshua B. Tenenbaum, and Fredo Durand · 2021
Cited alongside, same era.
Nerfdiff: Single-image view synthesis with nerf-guided distillation from 3d-aware diffusion
Jiatao Gu, Alex Trevithick, Kai-En Lin, Josh Susskind, Christian Theobalt, Lingjie Liu, and Ravi Ramamoorthi · 2023
Later among the works it cites.
Sparsenerf: Distilling depth ranking for few-shot novel view synthesis
Guangcong, Zhaoxi Chen, Chen Change Loy, and Ziwei Liu · 2023
Later among the works it cites.
Neo 360: Neural fields for sparse view synthesis of outdoor scenes
Muhammad Zubair Irshad, Sergey Zakharov, Katherine Liu, Vitor Guizilini, Thomas Kollar, Adrien Gaidon, Zsolt Kira, and Rares Ambrus · 2023
Later among the works it cites.
Scalable adaptive computation for iterative generation
Allan Jabri, David J. Fleet, and Ting Chen · 2023
Later among the works it cites.
Holodiffusion: Training a 3D diffusion model using 2D images
Animesh Karnewar, Andrea Vedaldi, David Novotny, and Niloy Mitra · 2023
Later among the works it cites.
Repurposing diffusion-based image generators for monocular depth estimation, 2023
Bingxin Ke, Anton Obukhov, Shengyu Huang, Nando Metzger, Rodrigo Caye Daudt, and Konrad Schindler · 2023
Later among the works it cites.
3d gaussian splatting for real-time radiance field rendering
Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, and George Drettakis · 2023
Later among the works it cites.
ZeroNVS: Zero-shot 360-degree view synthesis from a single real image
Kyle Sargent, Zizhang Li, Tanmay Shah, Charles Herrmann, Hong-Xing Yu, Yunzhi Zhang, Eric Ryan Chan, Dmitry Lagun, Li Fei-Fei, Deqing Sun, and Jiajun Wu · 2023
Later among the works it cites.
Zero-shot metric depth with a field-of-view conditioned diffusion model, 2023
Saurabh Saxena, Junhwa Hur, Charles Herrmann, Deqing Sun, and David J. Fleet · 2023
Later among the works it cites.
SimpleNeRF: Regularizing sparse input neural radiance fields with simpler solutions
Nagabhushan Somraj, Adithyan Karanayil, and Rajiv Soundararajan · 2023
Later among the works it cites.
Diffusion with forward models: Solving stochastic inverse problems without direct supervision
Ayush Tewari, Tianwei Yin, George Cazenavette, Semon Rezchikov, Joshua B. Tenenbaum, Frédo Durand, William T. Freeman, and Vincent Sitzmann · 2023
Later among the works it cites.
Reconfusion: 3d reconstruction with diffusion priors
Rundi Wu, Ben Mildenhall, Philipp Henzler, Keunhong Park, Ruiqi Gao, Daniel Watson, Pratul P. Srinivasan, Dor Verbin, Jonathan T. Barron, Ben Poole, and Aleksander Holynski · 2023
Later among the works it cites.
DiffusioNeRF: Regularizing Neural Radiance Fields with Denoising Diffusion Models
Jamie Wynn and Daniyar Turmukhambetov · 2023
Later among the works it cites.
Dmv3d: Denoising multi-view diffusion using 3d large reconstruction model, 2023
Yinghao Xu, Hao Tan, Fujun Luan, Sai Bi, Peng Wang, Jiahao Li, Zifan Shi, Kalyan Sunkavalli, Gordon Wetzstein, Zexiang Xu, and Kai Zhang · 2023
Later among the works it cites.
Freenerf: Improving few-shot neural rendering with free frequency regularization
Jiawei Yang, Marco Pavone, and Yue Wang · 2023
Later among the works it cites.
Metric3d: Towards zero-shot metric 3d prediction from a single image
Wei Yin, Chi Zhang, Hao Chen, Zhipeng Cai, Gang Yu, Kaixuan Wang, Xiaozhi Chen, and Chunhua Shen · 2023
Later among the works it cites.
Dreamsparse: Escaping from plato’s cave with 2d frozen diffusion model given sparse views
Paul Yoo, Jiaxian Guo, Yutaka Matsuo, and Shixiang Shane Gu · 2023
Later among the works it cites.
Mvimgnet: A large-scale dataset of multi-view images
Xianggang Yu, Mutian Xu, Yidan Zhang, Haolin Liu, Chongjie Ye, Yushuang Wu, Zizheng Yan, Tianyou Liang, Guanying Chen, Shuguang Cui, and Xiaoguang Han · 2023
Later among the works it cites.
https://github.com/webdataset/webdataset , 2024
Webdataset · 2024
Later among the works it cites.
STORM: Spatio-temporal reconstruction model for large-scale outdoor scenes
Anonymous · 2024
Later among the works it cites.
Mvsplat: Efficient 3d gaussian splatting from sparse multi-view images
Yuedong Chen, Haofei Xu, Chuanxia Zheng, Bohan Zhuang, Marc Pollefeys, Andreas Geiger, Tat-Jen Cham, and Jianfei Cai · 2024
Later among the works it cites.
Geowizard: Unleashing the diffusion priors for 3d geometry estimation from a single image
Xiao Fu, Wei Yin, Mu Hu, Kaixuan Wang, Yuexin Ma, Ping Tan, Shaojie Shen, Dahua Lin, and Xiaoxiao Long · 2024
Later among the works it cites.
Cat3d: Create anything in 3d with multi-view diffusion models
Ruiqi Gao*, Aleksander Holynski*, Philipp Henzler, Arthur Brussee, Ricardo Martin-Brualla, Pratul P. Srinivasan, Jonathan T. Barron, and Ben Poole* · 2024
Later among the works it cites.
Depthfm: Fast monocular depth estimation with flow matching, 2024
Ming Gui, Johannes S, Fischer, Ulrich Prestel, Pingchuan Ma, Olga Grebenkova Dmytro Kotovenko, Stefan Andreas Baumann, Vincent Tao Hu, and Björn Ommer · 2024
Later among the works it cites.
Grin: Zero-shot metric depth with pixel-level diffusion, 2024
Vitor Guizilini, Pavel Tokmakov, Achal Dave, and Rares Ambrus · 2024
Later among the works it cites.
Nerf-mae: Masked autoencoders for self-supervised 3d representation learning for neural radiance fields
Muhammad Zubair Irshad, Sergey Zakharov, Vitor Guizilini, Adrien Gaidon, Zsolt Kira, and Rares Ambrus · 2024
Later among the works it cites.
UniDepth: Universal monocular metric depth estimation
Luigi Piccinelli, Yung-Hsu Yang, Christos Sakaridis, Mattia Segu, Siyuan Li, Luc Van Gool, and Fisher Yu · 2024
Later among the works it cites.
Learning temporally consistent video depth from video diffusion priors, 2024
Jiahao Shao, Yuanbo Yang, Hongyu Zhou, Youmin Zhang, Yujun Shen, Matteo Poggi, and Yiyi Liao · 2024
Later among the works it cites.
Rgbd objects in the wild: Scaling real-world 3d object learning from rgb-d videos, 2024
Hongchi Xia, Yang Fu, Sifei Liu, and Xiaolong Wang · 2024
Later among the works it cites.
Monst3r: A simple approach for estimating geometry in the presence of motion
Junyi Zhang, Charles Herrmann, Junhwa Hur, Varun Jampani, Trevor Darrell, Forrester Cole, Deqing Sun, and Ming-Hsuan Yang · 2024
Later among the works it cites.