Fetching the paper…
Reading the bibliography…
A comprehensive understanding of 3D scenes is crucial in autonomous vehicles (AVs), and recent models for 3D semantic occupancy prediction have successfully addressed the challenge of describing real-world objects with varied shapes and classes.
R. Yadav, A. Vierling, and K. Berns, “Radar+ rgb fusion for robust object detection in autonomous vehicle,” in 2020 IEEE International Conference on Image Processing (ICIP) . IEEE, 2020, pp. 1986–1990
1990
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 2117–2125
2017
Earlier work this paper cites.
J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei, “Deformable convolutional networks,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 764–773
2017
Earlier work this paper cites.
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2980–2988
2017
Earlier work this paper cites.
Y. Zhou and O. Tuzel, “Voxelnet: End-to-end learning for point cloud based 3d object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 4490–4499
2018
Earlier work this paper cites.
C. R. Qi, W. Liu, C. Wu, H. Su, and L. J. Guibas, “Frustum pointnets for 3d object detection from rgb-d data,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 918–927
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
M. Liang, B. Yang, S. Wang, and R. Urtasun, “Deep continuous fusion for multi-sensor 3d object detection,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 641–656
2018
Earlier work this paper cites.
J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 7132–7141
2018
Earlier work this paper cites.
M. Berman, A. R. Triki, and M. B. Blaschko, “The lovász-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 4413–4421
2018
Earlier work this paper cites.
A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, “Pointpillars: Fast encoders for object detection from point clouds,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 12 697–12 705
2019
Earlier work this paper cites.
X. Zhao, Z. Liu, R. Hu, and K. Huang, “3d object detection using scale invariant and feature reweighting networks,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, no. 01, 2019, pp. 9267–9274
2019
Earlier work this paper cites.
V. A. Sindagi, Y. Zhou, and O. Tuzel, “Mvx-net: Multimodal voxelnet for 3d object detection,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 7276–7282
2019
Earlier work this paper cites.
B. Major, D. Fontijne, A. Ansari, R. Teja Sukhavasi, R. Gowaikar, M. Hamilton, S. Lee, S. Grzechnik, and S. Subramanian, “Vehicle detection with automotive radar using deep learning on range-azimuth-doppler tensors,” in Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops , 2019, pp. 0–0
2019
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 Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 621–11 631
2020
Earlier work this paper cites.
J. Philion and S. Fidler, “Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XIV 16 . Springer, 2020, pp. 194–210
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
Y. Zhang, Z. Zhou, P. David, X. Yue, Z. Xi, B. Gong, and H. Foroosh, “Polarnet: An improved grid representation for online lidar point clouds semantic segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 9601–9610
2020
Earlier work this paper cites.
H. Tang, Z. Liu, S. Zhao, Y. Lin, J. Lin, H. Wang, and S. Han, “Searching efficient 3d architectures with sparse point-voxel convolution,” in European conference on computer vision . Springer, 2020, pp. 685–702
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
S. Vora, A. H. Lang, B. Helou, and O. Beijbom, “Pointpainting: Sequential fusion for 3d object detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 4604–4612
2020
Earlier work this paper cites.
M. Bijelic, T. Gruber, F. Mannan, F. Kraus, W. Ritter, K. Dietmayer, and F. Heide, “Seeing through fog without seeing fog: Deep multimodal sensor fusion in unseen adverse weather,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 682–11 692
2020
Earlier work this paper cites.
Z. Murez, T. Van As, J. Bartolozzi, A. Sinha, V. Badrinarayanan, and A. Rabinovich, “Atlas: End-to-end 3d scene reconstruction from posed images,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VII 16 . Springer, 2020, pp. 414–431
2020
Cited alongside, same era.
L. Roldão, R. de Charette, and A. Verroust-Blondet, “Lmscnet: Lightweight multiscale 3d semantic completion,” 2020
2020
Cited alongside, same era.
J. Li, K. Han, P. Wang, Y. Liu, and X. Yuan, “Anisotropic convolutional networks for 3d semantic scene completion,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 3351–3359
2020
Cited alongside, same era.
X. Chen, K.-Y. Lin, C. Qian, G. Zeng, and H. Li, “3d sketch-aware semantic scene completion via semi-supervised structure prior,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 4193–4202
2020
Cited alongside, same era.
Y. Li, H. Bao, Z. Ge, J. Yang, J. Sun, and Z. Li, “Bevstereo: Enhancing depth estimation in multi-view 3d object detection with temporal stereo,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 2, 2023, pp. 1486–1494
2023
Later among the works it cites.
Y. Huang, W. Zheng, Y. Zhang, J. Zhou, and J. Lu, “Tri-perspective view for vision-based 3d semantic occupancy prediction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 9223–9232
2023
Later among the works it cites.
2023
Later among the works it cites.
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
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2021
Cited alongside, same era.
S. F. Bhat, I. Alhashim, and P. Wonka, “Adabins: Depth estimation using adaptive bins,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 4009–4018
2021
Cited alongside, same era.
R. Cheng, R. Razani, E. Taghavi, E. Li, and B. Liu, “2-s3net: Attentive feature fusion with adaptive feature selection for sparse semantic segmentation network,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 12 547–12 556
2021
Cited alongside, same era.
Q. Chen, S. Vora, and O. Beijbom, “Polarstream: Streaming object detection and segmentation with polar pillars,” Advances in Neural Information Processing Systems , vol. 34, pp. 26 871–26 883, 2021
2021
Cited alongside, same era.
X. Yan, J. Gao, J. Li, R. Zhang, Z. Li, R. Huang, and S. Cui, “Sparse single sweep lidar point cloud segmentation via learning contextual shape priors from scene completion,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 4, 2021, pp. 3101–3109
2021
Cited alongside, same era.
S. Li, X. Chen, Y. Liu, D. Dai, C. Stachniss, and J. Gall, “Multi-scale interaction for real-time lidar data segmentation on an embedded platform,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 738–745, 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
R. Nabati and H. Qi, “Centerfusion: Center-based radar and camera fusion for 3d object detection,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2021, pp. 1527–1536
2021
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Li, Z. Ge, G. Yu, J. Yang, Z. Wang, Y. Shi, J. Sun, and Z. Li, “Bevdepth: Acquisition of reliable depth for multi-view 3d object detection,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 2, 2023, pp. 1477–1485
2023
Later among the works it cites.
C. Yang, Y. Chen, H. Tian, C. Tao, X. Zhu, Z. Zhang, G. Huang, H. Li, Y. Qiao, L. Lu et al. , “Bevformer v2: Adapting modern image backbones to bird’s-eye-view recognition via perspective supervision,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 17 830–17 839
2023
Later among the works it cites.
2023
Later among the works it cites.
D. Ye, Z. Zhou, W. Chen, Y. Xie, Y. Wang, P. Wang, and H. Foroosh, “Lidarmultinet: Towards a unified multi-task network for lidar perception,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 3, 2023, pp. 3231–3240
2023
Later among the works it cites.
T. Khurana, P. Hu, D. Held, and D. Ramanan, “Point cloud forecasting as a proxy for 4d occupancy forecasting,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
Z. Liu, H. Tang, A. Amini, X. Yang, H. Mao, D. L. Rus, and S. Han, “Bevfusion: Multi-task multi-sensor fusion with unified bird’s-eye view representation,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 2774–2781
2023
Later among the works it cites.
2023
Later among the works it cites.
X. Chen, T. Zhang, Y. Wang, Y. Wang, and H. Zhao, “Futr3d: A unified sensor fusion framework for 3d detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 172–181
2023
Later among the works it cites.
A. W. Harley, Z. Fang, J. Li, R. Ambrus, and K. Fragkiadaki, “Simple-bev: What really matters for multi-sensor bev perception?” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 2759–2765
2023
Later among the works it cites.
X. Wang, Z. Zhu, W. Xu, Y. Zhang, Y. Wei, X. Chi, Y. Ye, D. Du, J. Lu, and X. Wang, “Openoccupancy: A large scale benchmark for surrounding semantic occupancy perception,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 17 850–17 859
2023
Later among the works it cites.
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 Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2023, pp. 9087–9098
2023
Later among the works it cites.
2023
Later among the works it cites.