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3D semantic scene understanding is a fundamental challenge in computer vision.
Ray tracing volume densities
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Dense 3d semantic mapping of indoor scenes from rgb-d images
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Fayao Liu, Chunhua Shen, Guosheng Lin, and Ian Reid · 2015
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Voxnet: A 3d convolutional neural network for real-time object recognition
Daniel Maturana and Sebastian Scherer · 2015
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The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
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Deep residual learning for image recognition
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Charles R Qi, Hao Su, Matthias Nießner, Angela Dai, Mengyuan Yan, and Leonidas J Guibas · 2016
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Perspective transformer nets: Learning single-view 3d object reconstruction without 3d supervision
Xinchen Yan, Jimei Yang, Ersin Yumer, Yijie Guo, and Honglak Lee · 2016
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A point set generation network for 3d object reconstruction from a single image
Haoqiang Fan, Hao Su, and Leonidas J Guibas · 2017
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Unsupervised monocular depth estimation with left-right consistency
Clément Godard, Oisin Mac Aodha, and Gabriel J Brostow · 2017
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Semi-supervised deep learning for monocular depth map prediction
Yevhen Kuznietsov, Jorg Stuckler, and Bastian Leibe · 2017
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Semanticfusion: Dense 3d semantic mapping with convolutional neural networks
John McCormac, Ankur Handa, Andrew Davison, and Stefan Leutenegger · 2017
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Semantic scene completion from a single depth image
Shuran Song, Fisher Yu, Andy Zeng, Angel X Chang, Manolis Savva, and Thomas Funkhouser · 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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Unsupervised learning of depth and ego-motion from video
Tinghui Zhou, Matthew Brown, Noah Snavely, and David G Lowe · 2017
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Birdnet: a 3d object detection framework from lidar information
Jorge Beltrán, Carlos Guindel, Francisco Miguel Moreno, Daniel Cruzado, Fernando Garcia, and Arturo De La Escalera · 2018
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Deep ordinal regression network for monocular depth estimation
Huan Fu, Mingming Gong, Chaohui Wang, Kayhan Batmanghelich, and Dacheng Tao · 2018
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See and think: Disentangling semantic scene completion
Shice Liu, Yu Hu, Yiming Zeng, Qiankun Tang, Beibei Jin, Yinhe Han, and Xiaowei Li · 2018
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Squeezeseg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud
Bichen Wu, Alvin Wan, Xiangyu Yue, and Kurt Keutzer · 2018
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Deep virtual stereo odometry: Leveraging deep depth prediction for monocular direct sparse odometry
Nan Yang, Rui Wang, Jorg Stuckler, and Daniel Cremers · 2018
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Unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction
Huangying Zhan, Ravi Garg, Chamara Saroj Weerasekera, Kejie Li, Harsh Agarwal, and Ian Reid · 2018
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Efficient semantic scene completion network with spatial group convolution
Jiahui Zhang, Hao Zhao, Anbang Yao, Yurong Chen, Li Zhang, and Hongen Liao · 2018
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3d scene graph: A structure for unified semantics, 3d space, and camera
Iro Armeni, Zhi-Yang He, JunYoung Gwak, Amir R Zamir, Martin Fischer, Jitendra Malik, and Silvio Savarese · 2019
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Semantickitti: A dataset for semantic scene understanding of lidar sequences
Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Cyrill Stachniss, and Jurgen Gall · 2019
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Patch-wise attention network for monocular depth estimation
Sihaeng Lee, Janghyeon Lee, Byungju Kim, Eojindl Yi, and Junmo Kim · 2021
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Semi-supervised implicit scene completion from sparse lidar, 2021
Pengfei Li, Yongliang Shi, Tianyu Liu, Hao Zhao, Guyue Zhou, and Ya-Qin Zhang · 2021
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Hr-depth: High resolution self-supervised monocular depth estimation
Xiaoyang Lyu, Liang Liu, Mengmeng Wang, Xin Kong, Lina Liu, Yong Liu, Xinxin Chen, and Yi Yuan · 2021
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Diffuser: Multi-view 2d-to-3d label diffusion for semantic scene segmentation
Ruben Mascaro, Lucas Teixeira, and Margarita Chli · 2021
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Semantic scene completion using local deep implicit functions on lidar data
Christoph B Rist, David Emmerichs, Markus Enzweiler, and Dariu M Gavrila · 2021
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Digging into self-supervised monocular depth estimation
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Pytorch: An imperative style, high-performance deep learning library
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Sparse single sweep lidar point cloud segmentation via learning contextual shape priors from scene completion
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pixelnerf: Neural radiance fields from one or few images
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In-place scene labelling and understanding with implicit scene representation
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Self-supervised monocular depth estimation with internal feature fusion
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Monoscene: Monocular 3d semantic scene completion
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Panoptic nerf: 3d-to-2d label transfer for panoptic urban scene segmentation
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Panoptic Neural Fields: A Semantic Object-Aware Neural Scene Representation
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Kitti-360: A novel dataset and benchmarks for urban scene understanding in 2d and 3d
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3d semantic scene completion: A survey
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Dm-nerf: 3d scene geometry decomposition and manipulation from 2d images
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