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Accurate perception of the dynamic environment is a fundamental task for autonomous driving and robot systems.
Are we ready for autonomous driving? the kitti vision benchmark suite
A. Geiger, P. Lenz, and R. Urtasun · 2012
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Depth map prediction from a single image using a multi-scale deep network
D. Eigen, C. Puhrsch, and R. Fergus · 2014
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Flownet: Learning optical flow with convolutional networks
A. Dosovitskiy, P. Fischer, E. Ilg, P. Hausser, C. Hazirbas, V. Golkov, P. Van Der Smagt, D. Cremers, and T. Brox · 2015
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A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
N. Mayer, E. Ilg, P. Hausser, P. Fischer, D. Cremers, A. Dosovitskiy, and T. Brox · 2016
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Feature pyramid networks for object detection, 2017
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie · 2017
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Unsupervised learning of depth and ego-motion from video
T. Zhou, M. Brown, N. Snavely, and D. G. Lowe · 2017
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Digging into self-supervised monocular depth prediction
C. Godard, O. Mac Aodha, M. Firman, and G. J. Brostow · 2019
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Semantickitti: A dataset for semantic scene understanding of lidar sequences
J. Behley, M. Garbade, A. Milioto, J. Quenzel, S. Behnke, C. Stachniss, and J. Gall · 2019
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Digging into self-supervised monocular depth estimation
C. Godard, O. Mac Aodha, M. Firman, and G. J. Brostow · 2019
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What matters in unsupervised optical flow
R. Jonschkowski, A. Stone, J. T. Barron, A. Gordon, K. Konolige, and A. Angelova · 2020
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nuscenes: A multimodal dataset for autonomous driving
H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom · 2020
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What matters in unsupervised optical flow
R. Jonschkowski, A. Stone, J. T. Barron, A. Gordon, K. Konolige, and A. Angelova · 2020
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Nerf: Representing scenes as neural radiance fields for view synthesis
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng · 2021
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Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction
P. Wang, L. Liu, Y. Liu, C. Theobalt, T. Komura, and W. Wang · 2021
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Plenoxels: Radiance fields without neural networks
S. Fridovich-Keil, A. Yu, M. Tancik, Q. Chen, B. Recht, and A. Kanazawa · 2022
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Instant neural graphics primitives with a multiresolution hash encoding
T. Müller, A. Evans, C. Schied, and A. Keller · 2022
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Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction
C. Sun, M. Sun, and H. Chen · 2022
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Spidr: Sdf-based neural point fields for illumination and deformation
R. Liang, J. Zhang, H. Li, C. Yang, Y. Guan, and N. Vijaykumar · 2022
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A convnet for the 2020s
Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie · 2022
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Vision transformer with deformable attention
Z. Xia, X. Pan, S. Song, L. E. Li, and G. Huang · 2022
Cited alongside, same era.
Monoscene: Monocular 3d semantic scene completion
A.-Q. Cao and R. De Charette · 2022
Cotracker: It is better to track together
N. Karaev, I. Rocco, B. Graham, N. Neverova, A. Vedaldi, and C. Rupprecht · 2023
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Scenerf: Self-supervised monocular 3d scene reconstruction with radiance fields
A.-Q. Cao and R. de Charette · 2023
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Behind the scenes: Density fields for single view reconstruction
F. Wimbauer, N. Yang, C. Rupprecht, and D. Cremers · 2023
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pixelsplat: 3d gaussian splats from image pairs for scalable generalizable 3d reconstruction
D. Charatan, S. Li, A. Tagliasacchi, and V. Sitzmann · 2023
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Occformer: Dual-path transformer for vision-based 3d semantic occupancy prediction
Y. Zhang, Z. Zhu, and D. Du · 2023
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Cited alongside, same era.
Surroundocc: Multi-camera 3d occupancy prediction for autonomous driving
Y. Wei, L. Zhao, W. Zheng, Z. Zhu, J. Zhou, and J. Lu · 2023
Cited alongside, same era.
A simple attempt for 3d occupancy estimation in autonomous driving
W. Gan, N. Mo, H. Xu, and N. Yokoya · 2023
Cited alongside, same era.
Occnerf: Advancing 3d occupancy prediction in lidar-free environments
C. Zhang, J. Yan, Y. Wei, J. Li, L. Liu, Y. Tang, Y. Duan, and J. Lu · 2023
Cited alongside, same era.
Selfocc: Self-supervised vision-based 3d occupancy prediction
Y. Huang, W. Zheng, B. Zhang, J. Zhou, and J. Lu · 2023
Cited alongside, same era.
Renderocc: Vision-centric 3d occupancy prediction with 2d rendering supervision
M. Pan, J. Liu, R. Zhang, P. Huang, X. Li, L. Liu, and S. Zhang · 2023
Cited alongside, same era.
Scene as occupancy
W. Tong, C. Sima, T. Wang, L. Chen, S. Wu, H. Deng, Y. Gu, L. Lu, P. Luo, D. Lin, et al · 2023
Cited alongside, same era.
Later among the works it cites.
Designing bert for convolutional networks: Sparse and hierarchical masked modeling
K. Tian, Y. Jiang, Q. Diao, C. Lin, L. Wang, and Z. Yuan · 2023
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FB-OCC: 3D occupancy prediction based on forward-backward view transformation
Z. Li, Z. Yu, D. Austin, M. Fang, S. Lan, J. Kautz, and J. M. Alvarez · 2023
Later among the works it cites.
Surrounddepth: Entangling surrounding views for self-supervised multi-camera depth estimation
Y. Wei, L. Zhao, W. Zheng, Z. Zhu, Y. Rao, G. Huang, J. Lu, and J. Zhou · 2023
Later among the works it cites.
J. Li, X. He, C. Zhou, X. Cheng, Y. Wen, and D. Zhang · 2024
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ν \nu -dba: Neural implicit dense bundle adjustment enables image-only driving scene reconstruction, 2024
Y. Mao, B. Shen, Y. Yang, K. Wang, R. Xiong, Y. Liao, and Y. Wang · 2024
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Grounded sam: Assembling open-world models for diverse visual tasks
T. Ren, S. Liu, A. Zeng, J. Lin, K. Li, H. Cao, J. Chen, X. Huang, Y. Chen, F. Yan, et al · 2024
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Mvsplat: Efficient 3d gaussian splatting from sparse multi-view images
Y. Chen, H. Xu, C. Zheng, B. Zhuang, M. Pollefeys, A. Geiger, T.-J. Cham, and J. Cai · 2024
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Henet: Hybrid encoding for end-to-end multi-task 3d perception from multi-view cameras
Z. Xia, Z. Lin, X. Wang, Y. Wang, Y. Xing, S. Qi, N. Dong, and M.-H. Yang · 2024
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Fully sparse 3d occupancy prediction
H. Liu, Y. Chen, H. Wang, Z. Yang, T. Li, J. Zeng, L. Chen, H. Li, and L. Wang · 2024
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