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Scene completion and forecasting are two popular perception problems in research for mobile agents like autonomous vehicles.
B. Zheng, Y. Zhao, J. C. Yu, K. Ikeuchi, and S.-C. Zhu, “Beyond point clouds: Scene understanding by reasoning geometry and physics,” in CVPR , 2013, pp. 3127–3134
2013
Earlier work this paper cites.
X. Shi, Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, and W.-c. Woo, “Convolutional lstm network: A machine learning approach for precipitation nowcasting,” NIPS , vol. 28, 2015
2015
Earlier work this paper cites.
D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri, “Learning spatiotemporal features with 3d convolutional networks,” in ICCV , 2015, pp. 4489–4497
2015
Earlier work this paper cites.
S. Song, F. Yu, A. Zeng, A. X. Chang, M. Savva, and T. Funkhouser, “Semantic scene completion from a single depth image,” in CVPR , 2017, pp. 1746–1754
2017
Earlier work this paper cites.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in CVPR , 2017, pp. 652–660
2017
Earlier work this paper cites.
S. K. Ramakrishnan and K. Grauman, “Sidekick policy learning for active visual exploration,” in ECCV , 2018, pp. 413–430
2018
Earlier work this paper cites.
S. Song, A. Zeng, A. X. Chang, M. Savva, S. Savarese, and T. Funkhouser, “Im2pano3d: Extrapolating 360 structure and semantics beyond the field of view,” in CVPR , 2018, pp. 3847–3856
2018
Earlier work this paper cites.
X. Huang, X. Cheng, Q. Geng, B. Cao, D. Zhou, P. Wang, Y. Lin, and R. Yang, “The apolloscape dataset for autonomous driving,” in CVPR workshops , 2018, pp. 954–960
2018
Earlier work this paper cites.
J. Behley, M. Garbade, A. Milioto, J. Quenzel, S. Behnke, C. Stachniss, and J. Gall, “Semantickitti: A dataset for semantic scene understanding of lidar sequences,” in CVPR , 2019, pp. 9297–9307
2019
Earlier work this paper cites.
Z. Yang, J. Z. Pan, L. Luo, X. Zhou, K. Grauman, and Q. Huang, “Extreme relative pose estimation for rgb-d scans via scene completion,” in CVPR , 2019, pp. 4531–4540
2019
Earlier work this paper cites.
M. Schreiber, S. Hoermann, and K. Dietmayer, “Long-term occupancy grid prediction using recurrent neural networks,” in ICRA . IEEE, 2019, pp. 9299–9305
2019
Earlier work this paper cites.
M.-F. Chang, J. Lambert, P. Sangkloy, J. Singh, S. Bak, A. Hartnett, D. Wang, P. Carr, S. Lucey, D. Ramanan, et al. , “Argoverse: 3d tracking and forecasting with rich maps,” in CVPR , 2019, pp. 8748–8757
2019
Earlier work this paper cites.
W. Zeng, W. Luo, S. Suo, A. Sadat, B. Yang, S. Casas, and R. Urtasun, “End-to-end interpretable neural motion planner,” in CVPR , 2019, pp. 8660–8669
2019
Earlier work this paper cites.
Y. Huang, Z. Tang, D. Chen, K. Su, and C. Chen, “Batching soft iou for training semantic segmentation networks,” IEEE Signal Processing Lett. , vol. 27, pp. 66–70, 2019
2019
Earlier work this paper cites.
L. Roldao, R. de Charette, and A. Verroust-Blondet, “Lmscnet: Lightweight multiscale 3d semantic completion,” in 3DV . IEEE, 2020, pp. 111–119
2020
Earlier work this paper cites.
X. Wang, M. H. Ang, and G. H. Lee, “Point cloud completion by learning shape priors,” in IROS . IEEE, 2020, pp. 10 719–10 726
2020
Earlier work this paper cites.
X. Sun, S. Wang, M. Wang, Z. Wang, and M. Liu, “A novel coding architecture for lidar point cloud sequence,” IEEE Robot. Automat. Lett. , vol. 5, no. 4, pp. 5637–5644, 2020
2020
Earlier work this paper cites.
D. Deng and A. Zakhor, “Temporal lidar frame prediction for autonomous driving,” in 3DV . IEEE, 2020, pp. 829–837
2020
Earlier work this paper cites.
A. Sadat, S. Casas, M. Ren, X. Wu, P. Dhawan, and R. Urtasun, “Perceive, predict, and plan: Safe motion planning through interpretable semantic representations,” in ECCV . Springer, 2020, pp. 414–430
2020
Earlier work this paper cites.
H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” in CVPR , 2020, pp. 11 621–11 631
2020
Earlier work this paper cites.
P. Sun, H. Kretzschmar, X. Dotiwalla, A. Chouard, V. Patnaik, P. Tsui, J. Guo, Y. Zhou, Y. Chai, B. Caine, et al. , “Scalability in perception for autonomous driving: Waymo open dataset,” in CVPR , 2020, pp. 2446–2454
2020
Earlier work this paper cites.
2020
Cited alongside, same era.
Q.-H. Pham, P. Sevestre, R. S. Pahwa, H. Zhan, C. H. Pang, Y. Chen, A. Mustafa, V. Chandrasekhar, and J. Lin, “A 3d dataset: Towards autonomous driving in challenging environments,” in ICRA . IEEE, 2020, pp. 2267–2273
2020
Cited alongside, same era.
T. Salzmann, B. Ivanovic, P. Chakravarty, and M. Pavone, “Trajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data,” in ECCV . Springer, 2020, pp. 683–700
2020
Cited alongside, same era.
A. Hu, Z. Murez, N. Mohan, S. Dudas, J. Hawke, V. Badrinarayanan, R. Cipolla, and A. Kendall, “Fiery: Future instance prediction in bird’s-eye view from surround monocular cameras,” in CVPR , 2021, pp. 15 273–15 282
2021
Cited alongside, same era.
T. Khurana, P. Hu, A. Dave, J. Ziglar, D. Held, and D. Ramanan, “Differentiable raycasting for self-supervised occupancy forecasting,” in ECCV . Springer, 2022, pp. 353–369
2022
Later among the works it cites.
B. Fei, W. Yang, W.-M. Chen, Z. Li, Y. Li, T. Ma, X. Hu, and L. Ma, “Comprehensive review of deep learning-based 3d point cloud completion processing and analysis,” IEEE Trans. Intell. Transport. Syst. , 2022
2022
Later among the works it cites.
R. Mahjourian, J. Kim, Y. Chai, M. Tan, B. Sapp, and D. Anguelov, “Occupancy flow fields for motion forecasting in autonomous driving,” IEEE Robot. Automat. Lett. , vol. 7, no. 2, pp. 5639–5646, 2022
2022
Later among the works it cites.
X. Weng, J. Nan, K.-H. Lee, R. McAllister, A. Gaidon, N. Rhinehart, and K. M. Kitani, “S2net: Stochastic sequential pointcloud forecasting,” in ECCV . Springer, 2022, pp. 549–564
2022
Later among the works it cites.
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M. Popović, F. Thomas, S. Papatheodorou, N. Funk, T. Vidal-Calleja, and S. Leutenegger, “Volumetric occupancy mapping with probabilistic depth completion for robotic navigation,” IEEE Robot. Automat. Lett. , vol. 6, no. 3, pp. 5072–5079, 2021
2021
Cited alongside, same era.
R. Cheng, C. Agia, Y. Ren, X. Li, and L. Bingbing, “S3cnet: A sparse semantic scene completion network for lidar point clouds,” in CoRL . PMLR, 2021, pp. 2148–2161
2021
Cited alongside, same era.
C. Zhang, M. Fiore, I. Murray, and P. Patras, “Cloudlstm: A recurrent neural model for spatiotemporal point-cloud stream forecasting,” in AAAI , vol. 35, no. 12, 2021, pp. 10 851–10 858
2021
Cited alongside, same era.
M. Ye, T. Cao, and Q. Chen, “Tpcn: Temporal point cloud networks for motion forecasting,” in CVPR , 2021, pp. 11 318–11 327
2021
Cited alongside, same era.
X. Weng, J. Wang, S. Levine, K. Kitani, and N. Rhinehart, “Inverting the pose forecasting pipeline with spf2: Sequential pointcloud forecasting for sequential pose forecasting,” in CoRL . PMLR, 2021, pp. 11–20
2021
Cited alongside, same era.
S. Casas, A. Sadat, and R. Urtasun, “Mp3: A unified model to map, perceive, predict and plan,” in CVPR , 2021, pp. 14 403–14 412
2021
Cited alongside, same era.
J. Deng, S. Shi, P. Li, W. Zhou, Y. Zhang, and H. Li, “Voxel r-cnn: Towards high performance voxel-based 3d object detection,” in AAAI , vol. 35, no. 2, 2021, pp. 1201–1209
2021
Cited alongside, same era.
J. Houston, G. Zuidhof, L. Bergamini, Y. Ye, L. Chen, A. Jain, S. Omari, V. Iglovikov, and P. Ondruska, “One thousand and one hours: Self-driving motion prediction dataset,” in CoRL . PMLR, 2021, pp. 409–418
2021
Cited alongside, same era.
B. Mersch, X. Chen, J. Behley, and C. Stachniss, “Self-supervised point cloud prediction using 3d spatio-temporal convolutional networks,” in CoRL . PMLR, 2022, pp. 1444–1454
2022
Later among the works it cites.
Y. Liao, J. Xie, and A. Geiger, “Kitti-360: A novel dataset and benchmarks for urban scene understanding in 2d and 3d,” IEEE Trans. Pattern Anal. Machine Intell. , vol. 45, no. 3, pp. 3292–3310, 2022
2022
Later among the works it cites.
B. Ivanovic, K.-H. Lee, P. Tokmakov, B. Wulfe, R. Mcllister, A. Gaidon, and M. Pavone, “Heterogeneous-agent trajectory forecasting incorporating class uncertainty,” in IROS . IEEE, 2022, pp. 12 196–12 203
2022
Later among the works it cites.
B. Zhou and P. Krähenbühl, “Cross-view transformers for real-time map-view semantic segmentation,” in CVPR , 2022, pp. 13 760–13 769
2022
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2023
Closest in time.
Y. Hu, J. Yang, L. Chen, K. Li, C. Sima, X. Zhu, S. Chai, S. Du, T. Lin, W. Wang, et al. , “Planning-oriented autonomous driving,” in CVPR , 2023, pp. 17 853–17 862
2023
Closest in time.
Y. Li, Z. Yu, C. Choy, C. Xiao, J. M. Alvarez, S. Fidler, C. Feng, and A. Anandkumar, “Voxformer: Sparse voxel transformer for camera-based 3d semantic scene completion,” in CVPR , 2023, pp. 9087–9098
2023
Closest in time.
T. Khurana, P. Hu, D. Held, and D. Ramanan, “Point cloud forecasting as a proxy for 4d occupancy forecasting,” in CVPR , 2023, pp. 1116–1124
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Y. Huang, W. Zheng, Y. Zhang, J. Zhou, and J. Lu, “Tri-perspective view for vision-based 3d semantic occupancy prediction,” in CVPR , 2023, pp. 9223–9232
2023
Closest in time.
2023
Closest in time.
Y. Wei, L. Zhao, W. Zheng, Z. Zhu, J. Zhou, and J. Lu, “Surroundocc: Multi-camera 3d occupancy prediction for autonomous driving,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 21 729–21 740
2023
Closest in time.
Z. Xia, Y. Liu, X. Li, X. Zhu, Y. Ma, Y. Li, Y. Hou, and Y. Qiao, “Scpnet: Semantic scene completion on point cloud,” in CVPR , 2023, pp. 17 642–17 651
2023
Closest in time.
C. Chen, X. Liu, Y. Li, L. Ding, and C. Feng, “Deepmapping2: Self-supervised large-scale lidar map optimization,” in CVPR , 2023, pp. 9306–9316
2023
Closest in time.