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Depth completion upgrades sparse depth measurements into dense depth maps guided by a conventional image.
Tweedie’s formula and selection bias
Bradley Efron · 2011
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Are we ready for autonomous driving? the KITTI vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
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Indoor segmentation and support inference from RGBD images
Pushmeet Kohli Nathan Silberman, Derek Hoiem and Rob Fergus · 2012
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Adam: A method for stochastic optimization
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Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 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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ScanNet: Richly-annotated 3d reconstructions of indoor scenes
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner · 2017
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Learning affinity via spatial propagation networks
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Sparsity invariant CNNs
Jonas Uhrig, Nick Schneider, Lukas Schneider, Uwe Franke, Thomas Brox, and Andreas Geiger · 2017
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Depth estimation via affinity learned with convolutional spatial propagation network
Xinjing Cheng, Peng Wang, and Ruigang Yang · 2018
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In defense of classical image processing: Fast depth completion on the cpu
Jason Ku, Ali Harakeh, and Steven L Waslander · 2018
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Sparse-to-dense: Depth prediction from sparse depth samples and a single image
Fangchang Ma and Sertac Karaman · 2018
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Self-supervised sparse-to-dense: Self-supervised depth completion from LiDAR and monocular camera
Fangchang Ma, Guilherme Venturelli Cavalheiro, and Sertac Karaman · 2018
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Deep depth completion of a single rgb-d image
Yinda Zhang and Thomas A. Funkhouser · 2018
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Learning joint 2d-3d representations for depth completion
Yun Chen, Bin Yang, Ming Liang, and Raquel Urtasun · 2019
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CSPN++: Learning context and resource aware convolutional spatial propagation networks for depth completion
Xinjing Cheng, Peng Wang, Chenye Guan, and Ruigang Yang · 2019
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Deep convolutional compressed sensing for lidar depth completion
Nathaniel Chodosh, Chaoyang Wang, and Simon Lucey · 2019
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Depth coefficients for depth completion
Saif Imran, Yunfei Long, Xiaoming Liu, and Daniel Morris · 2019
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Evaluation of cnn-based single-image depth estimation methods
Tobias Koch, Lukas Liebel, Friedrich Fraundorfer, and Marco Körner · 2019
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DeepLiDAR: Deep surface normal guided depth prediction for outdoor scene from sparse LiDAR data and single color image
Jiaxiong Qiu, Zhaopeng Cui, Yinda Zhang, Xingdi Zhang, Shuaicheng Liu, Bing Zeng, and Marc Pollefeys · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Depth completion from sparse LiDAR data with depth-normal constraints
Yan Xu, Xinge Zhu, Jianping Shi, Guofeng Zhang, Hujun Bao, and Hongsheng Li · 2019
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Yohann Cabon, Naila Murray, and Martin Humenberger · 2020
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3d packing for self-supervised monocular depth estimation
Vitor Guizilini, Rares Ambrus, Sudeep Pillai, Allan Raventos, and Adrien Gaidon · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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A multi-scale guided cascade hourglass network for depth completion
Ang Li, Zejian Yuan, Yonggen Ling, Wanchao Chi, Chong Zhang, et al · 2020
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FCFR-Net: Feature fusion based coarse-to-fine residual learning for depth completion
Lina Liu, Xibin Song, Xiaoyang Lyu, Junwei Diao, Mengmeng Wang, Yong Liu, and Liangjun Zhang · 2020
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Non-local spatial propagation network for depth completion
Jinsun Park, Kyungdon Joo, Zhe Hu, Chi-Kuei Liu, and In So Kweon · 2020
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Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer
René Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, and Vladlen Koltun · 2020
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
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Learning guided convolutional network for depth completion
Jie Tang, Fei-Peng Tian, Wei Feng, Jian Li, and Ping Tan · 2020
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Unsupervised depth completion from visual inertial odometry
Alex Wong, Xiaohan Fei, Stephanie Tsuei, and Stefano Soatto · 2020
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DDP: Diffusion model for dense visual prediction
Yuanfeng Ji, Zhe Chen, Enze Xie, Lanqing Hong, Xihui Liu, Zhaoqiang Liu, Tong Lu, Zhenguo Li, and Ping Luo · 2023
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DySPN: Learning dynamic affinity for image-guided depth completion
Yuankai Lin, Hua Yang, Tao Cheng, Wending Zhou, and Zhouping Yin · 2023
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Flow matching for generative modeling
Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matthew Le · 2023
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DINOv2: Learning robust visual features without supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al · 2023
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Solving linear inverse problems provably via posterior sampling with latent diffusion models
Litu Rout, Negin Raoof, Giannis Daras, Constantine Caramanis, Alex Dimakis, and Sanjay Shakkottai · 2023
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Xin Xiong, Haipeng Xiong, Ke Xian, Chen Zhao, Zhiguo Cao, and Xin Li · 2020
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Diversedepth: Affine-invariant depth prediction using diverse data
Wei Yin, Xinlong Wang, Chunhua Shen, Yifan Liu, Zhi Tian, Songcen Xu, Changming Sun, and Dou Renyin · 2020
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alex Nichol · 2021
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An image is worth 16
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2021
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Depth completion with twin-surface extrapolation at occlusion boundaries
Saif Imran, Xiaoming Liu, and Daniel Morris · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
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LRRU: Long-short range recurrent updating networks for depth completion
Yufei Wang, Bo Li, Ge Zhang, Qi Liu, Gao Tao, and Yuchao Dai · 2023
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Aggregating feature point cloud for depth completion
Zhu Yu, Zehua Sheng, Zili Zhou, Lun Luo, Si-Yuan Cao, Hong Gu, Huaqi Zhang, and Hui-Liang Shen · 2023
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Unleashing text-to-image diffusion models for visual perception
Wenliang Zhao, Yongming Rao, Zuyan Liu, Benlin Liu, Jie Zhou, and Jiwen Lu · 2023
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Denoising diffusion models for plug-and-play image restoration
Yuanzhi Zhu, Kai Zhang, Jingyun Liang, Jiezhang Cao, Bihan Wen, Radu Timofte, and Luc Van Gool · 2023
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Revisiting depth completion from a stereo matching perspective for cross-domain generalization
Luca Bartolomei, Matteo Poggi, Andrea Conti, Fabio Tosi, and Stefano Mattoccia · 2024
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Depth pro: Sharp monocular metric depth in less than a second
Aleksei Bochkovskii, Amaël Delaunoy, Hugo Germain, Marcel Santos, Yichao Zhou, Stephan R Richter, and Vladlen Koltun · 2024
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Prompt-tuning latent diffusion models for inverse problems
Hyungjin Chung, Jong Chul Ye, Peyman Milanfar, and Mauricio Delbracio · 2024
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A survey on diffusion models for inverse problems, 2024
Giannis Daras, Hyungjin Chung, Chieh-Hsin Lai, Yuki Mitsufuji, Jong Chul Ye, Peyman Milanfar, Alexandros G. Dimakis, and Mauricio Delbracio · 2024
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The hugging face diffusion models course
Hugging Face · 2024
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Fine-tuning image-conditional diffusion models is easier than you think
Gonzalo Martin Garcia, Karim Abou Zeid, Christian Schmidt, Daan de Geus, Alexander Hermans, and Bastian Leibe · 2024
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SteeredMarigold: Steering diffusion towards depth completion of largely incomplete depth maps
Jakub Gregorek and Lazaros Nalpantidis · 2024
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Depthfm: Fast monocular depth estimation with flow matching
Ming Gui, Johannes S. Fischer, Ulrich Prestel, Pingchuan Ma, Dmytro Kotovenko, Olga Grebenkova, Stefan Andreas Baumann, Vincent Tao Hu, and Björn Ommer · 2024
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Repurposing diffusion-based image generators for monocular depth estimation
Bingxin Ke, Anton Obukhov, Shengyu Huang, Nando Metzger, Rodrigo Caye Daudt, and Konrad Schindler · 2024
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Joint pedestrian trajectory prediction through posterior sampling
Haotian Lin, Yixiao Wang, Mingxiao Huo, Chensheng Peng, Zhiyuan Liu, and Masayoshi Tomizuka · 2024
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DepthLab: From partial to complete
Zhiheng Liu, Ka Leong Cheng, Qiuyu Wang, Shuzhe Wang, Hao Ouyang, Bin Tan, Kai Zhu, Yujun Shen, Qifeng Chen, and Ping Luo · 2024
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DeCoTR: Enhancing depth completion with 2d and 3d attentions
Yunxiao Shi, Manish Kumar Singh, Hong Cai, and Fatih Porikli · 2024
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Bilateral propagation network for depth completion
Jie Tang, Fei-Peng Tian, Boshi An, Jian Li, and Ping Tan · 2024
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Tri-perspective view decomposition for geometry-aware depth completion
Zhiqiang Yan, Yuankai Lin, Kun Wang, Yupeng Zheng, Yufei Wang, Zhenyu Zhang, Jun Li, and Jian Yang · 2024
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Wonderworld: Interactive 3d scene generation from a single image
Hong-Xing Yu, Haoyi Duan, Charles Herrmann, William T Freeman, and Jiajun Wu · 2024
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Ogni-dc: Robust depth completion with optimization-guided neural iterations
Yiming Zuo and Jia Deng · 2024
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Zero-shot depth completion via test-time alignment with affine-invariant depth prior
Lee Hyoseok, Kyeong Seon Kim, Kwon Byung-Ki, and Tae-Hyun Oh · 2025
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Prompting depth anything for 4k resolution accurate metric depth estimation, 2025
Haotong Lin, Sida Peng, Jingxiao Chen, Songyou Peng, Jiaming Sun, Minghuan Liu, Hujun Bao, Jiashi Feng, Xiaowei Zhou, and Bingyi Kang · 2025
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