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Multi-modal 3D object detection models for automated driving have demonstrated exceptional performance on computer vision benchmarks like nuScenes.
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler, and R. Urtasun, “Monocular 3d object detection for autonomous driving,” in
2016
Earlier work this paper cites.
X. Chen, H. Ma, J. Wan, B. Li, and T. Xia, “Multi-view 3d object detection network for autonomous driving,” 2017
2017
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,” 2018
2018
Earlier work this paper cites.
K. Shin, Y. P. Kwon, and M. Tomizuka, “Roarnet: A robust 3d object detection based on region approximation refinement,” 2018
2018
Earlier work this paper cites.
J. Ku, M. Mozifian, J. Lee, A. Harakeh, and S. Waslander, “Joint 3d proposal generation and object detection from view aggregation,”
2018
Earlier work this paper cites.
D. Hendrycks and T. Dietterich, “Benchmarking neural network robustness to common corruptions and perturbations,”
2019
Earlier work this paper cites.
G. Brazil and X. Liu, “M3d-rpn: Monocular 3d region proposal network for object detection,” in
2019
Earlier work this paper cites.
K. Duan, S. Bai, L. Xie, H. Qi, Q. Huang, and Q. Tian, “Centernet: Keypoint triplets for object detection,” 2019
2019
Earlier work this paper cites.
Z. Tian, C. Shen, H. Chen, and T. He, “Fcos: Fully convolutional one-stage object detection,” 2019
2019
Earlier work this paper cites.
Z. Wang and K. Jia, “Frustum convnet: Sliding frustums to aggregate local point-wise features for amodal 3d object detection,” 2019
2019
Earlier work this paper cites.
M. Simon, K. Amende, A. Kraus, J. Honer, T. Sämann, H. Kaulbersch, S. Milz, and H. M. Gross, “Complexer-yolo: Real-time 3d object detection and tracking on semantic point clouds,” 2019
2019
Earlier work this paper cites.
V. A. Sindagi, Y. Zhou, and O. Tuzel, “Mvx-net: Multimodal voxelnet for 3d object detection,” 2019
2019
Earlier work this paper cites.
L. Xie, C. Xiang, Z. Yu, G. Xu, Z. Yang, D. Cai, and X. He, “Pi-rcnn: An efficient multi-sensor 3d object detector with point-based attentive cont-conv fusion module,” 2019
2019
Earlier work this paper cites.
Z. Liu, Z. Wu, and R. Tóth, “Smoke: Single-stage monocular 3d object detection via keypoint estimation,” in
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
2020
Earlier work this paper cites.
N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in
2020
Earlier work this paper cites.
S. Vora, A. H. Lang, B. Helou, and O. Beijbom, “Pointpainting: Sequential fusion for 3d object detection,” 2020
2020
Earlier work this paper cites.
J. H. Yoo, Y. Kim, J. Kim, and J. W. Choi, “3d-CVF: Generating joint camera and LiDAR features using cross-view spatial feature fusion for 3d object detection,” in
2020
Earlier work this paper cites.
S. Pang, D. Morris, and H. Radha, “Clocs: Camera-lidar object candidates fusion for 3d object detection,” 2020
2020
Earlier work this paper cites.
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
2020
Cited alongside, same era.
M. Pitropov, D. E. Garcia, J. Rebello, M. Smart, C. Wang, K. Czarnecki, and S. Waslander, “Canadian adverse driving conditions dataset,”
2020
Cited alongside, same era.
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,” 2020
2020
Cited alongside, same era.
A. Kumar, G. Brazil, and X. Liu, “Groomed-nms: Grouped mathematically differentiable nms for monocular 3d object detection,” in
2021
Cited alongside, same era.
S. Luo, H. Dai, L. Shao, and Y. Ding, “M3dssd: Monocular 3d single stage object detector,” in
2021
Y. Liu, T. Wang, X. Zhang, and J. Sun, “Petr: Position embedding transformation for multi-view 3d object detection,” in
2022
Later among the works it cites.
Z. Li, W. Wang, H. Li, E. Xie, C. Sima, T. Lu, Q. Yu, and J. Dai, “Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers,” 2022
2022
Later among the works it cites.
Y. Zhang, J. Chen, and D. Huang, “Cat-det: Contrastively augmented transformer for multi-modal 3d object detection,” 2022
2022
Later among the works it cites.
Y. Li, A. W. Yu, T. Meng, B. Caine, J. Ngiam, D. Peng, J. Shen, B. Wu, Y. Lu, D. Zhou, Q. V. Le, A. Yuille, and M. Tan, “Deepfusion: Lidar-camera deep fusion for multi-modal 3d object detection,” 2022
2022
Later among the works it cites.
Z. Chen, Z. Li, S. Zhang, L. Fang, Q. Jiang, F. Zhao, B. Zhou, and H. Zhao, “Autoalign: Pixel-instance feature aggregation for multi-modal 3d object detection,” 2022
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Cited alongside, same era.
T. Wang, X. Zhu, J. Pang, and D. Lin, “Fcos3d: Fully convolutional one-stage monocular 3d object detection,” in
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Y. Wang, V. Guizilini, T. Zhang, Y. Wang, H. Zhao, and J. Solomon, “Detr3d: 3d object detection from multi-view images via 3d-to-2d queries,” 2021
2021
Cited alongside, same era.
A. Paigwar, D. Sierra-Gonzalez, O. Erkent, and C. Laugier, “Frustum-pointpillars: A multi-stage approach for 3d object detection using rgb camera and lidar,” in
2021
Cited alongside, same era.
S. Xu, D. Zhou, J. Fang, J. Yin, Z. Bin, and L. Zhang, “Fusionpainting: Multimodal fusion with adaptive attention for 3d object detection,” 2021
2021
Cited alongside, same era.
C. Wang, C. Ma, M. Zhu, and X. Yang, “Pointaugmenting: Cross-modal augmentation for 3d object detection,” in
2021
Cited alongside, same era.
Z. Wang, Z. Zhao, Z. Jin, Z. Che, J. Tang, C. Shen, and Y. Peng, “Multi-stage fusion for multi-class 3d lidar detection,” in
2021
Cited alongside, same era.
2022
Later among the works it cites.
Z. Chen, Z. Li, S. Zhang, L. Fang, Q. Jiang, and F. Zhao, “Autoalignv2: Deformable feature aggregation for dynamic multi-modal 3d object detection,”
2022
Later among the works it cites.
Y. Li, X. Qi, Y. Chen, L. Wang, Z. Li, J. Sun, and J. Jia, “Voxel field fusion for 3d object detection,” 2022
2022
Later among the works it cites.
Y. Chen, H. Li, R. Gao, and D. Zhao, “Boost 3-d object detection via point clouds segmentation and fused 3-d giou-l1 loss,”
2022
Later among the works it cites.
K. Huang, B. Shi, X. Li, X. Li, S. Huang, and Y. Li, “Multi-modal sensor fusion for auto driving perception: A survey,” 2022
2022
Later among the works it cites.
K. Yu, T. Tang, H. Xie, Z. Lin, Z. Wu, Z. Xia, T. Liang, H. Sun, J. Deng, D. Hao, Y. Wang, X. Liang, and B. Wang, “Benchmarking the robustness of lidar-camera fusion for 3d object detection,”
2022
Later among the works it cites.
C. A. Diaz-Ruiz, Y. Xia, Y. You, J. Nino, J. Chen, J. Monica, X. Chen, K. Luo, Y. Wang, M. Emond, W.-L. Chao, B. Hariharan, K. Q. Weinberger, and M. Campbell, “Ithaca365: Dataset and driving perception under repeated and challenging weather conditions,” 2022
2022
Later among the works it cites.
M. Hahner, C. Sakaridis, M. Bijelic, F. Heide, F. Yu, D. Dai, and L. Van Gool, “LiDAR Snowfall Simulation for Robust 3D Object Detection,” in
2022
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
L. Wang, X. Zhang, Z. Song, J. Bi, G. Zhang, H. Wei, L. Tang, L. Yang, J. Li, C. Jia, and L. Zhao, “Multi-modal 3d object detection in autonomous driving: A survey and taxonomy,”
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Wang, Q. Mao, H. Zhu, J. Deng, Y. Zhang, J. Ji, H. Li, and Y. Zhang, “Multi-modal 3d object detection in autonomous driving: a survey,”
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
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,” 2023
2023
Later among the works it cites.