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Many LiDAR-based methods for detecting large objects, single-class object detection, or under easy situations were claimed to perform quite well.
Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion
Zhang, H.; Yang, D.; Yurtsever, E.; Redmill, K. A.; and Ümit Özgüner. 2021 · 2011
Earlier work this paper cites.
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A.; Lenz, P.; and Urtasun, R. 2012 · 2012
Earlier work this paper cites.
Microsoft COCO: Common Objects in Context
Lin, T.-Y.; Maire, M.; Belongie, S.; Hays, J.; Perona, P.; Ramanan, D.; Dollár, P.; and Zitnick, C. L. 2014 · 2014
Earlier work this paper cites.
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Charles, R. Q.; Su, H.; Kaichun, M.; and Guibas, L. J. 2017 · 2017
Earlier work this paper cites.
Multi-view 3D Object Detection Network for Autonomous Driving
Chen, X.; Ma, H.; Wan, J.; Li, B.; and Xia, T. 2017 · 2017
Earlier work this paper cites.
Mask R-CNN
He, K.; Gkioxari, G.; Dollár, P.; and Girshick, R. 2017 · 2017
Earlier work this paper cites.
Aggregated Residual Transformations for Deep Neural Networks
Xie, S.; Girshick, R.; Dollár, P.; Tu, Z.; and He, K. 2017 · 2017
Earlier work this paper cites.
Joint 3D Proposal Generation and Object Detection from View Aggregation
Ku, J.; Mozifian, M.; Lee, J.; Harakeh, A.; and Waslander, S. L. 2018 · 2018
Cited alongside, same era.
Frustum PointNets for 3D Object Detection from RGB-D Data
Qi, C. R.; Liu, W.; Wu, C.; Su, H.; and Guibas, L. J. 2018 · 2018
Cited alongside, same era.
MLOD: A multi-view 3D object detection based on robust feature fusion method
Deng, J.; and Czarnecki, K. 2019 · 2019
Cited alongside, same era.
Multi-Task Multi-Sensor Fusion for 3D Object Detection
Liang, M.; Yang, B.; Chen, Y.; Hu, R.; and Urtasun, R. 2019 · 2019
Cited alongside, same era.
Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection
Wang, Z.; and Jia, K. 2019 · 2019
Cited alongside, same era.
MonoFENet: Monocular 3D Object Detection With Feature Enhancement Networks
TANet: Robust 3D Object Detection from Point Clouds with Triple Attention
Liu, Z.; Zhao, X.; Huang, T.; Hu, R.; Zhou, Y.; and Bai, X. 2020 · 2020
Later among the works it cites.
PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection
Shi, S.; Guo, C.; Jiang, L.; Wang, Z.; Shi, J.; Wang, X.; and Li, H. 2020 · 2020
Later among the works it cites.
PointPainting: Sequential Fusion for 3D Object Detection
Vora, S.; Lang, A. H.; Helou, B.; and Beijbom, O. 2020 · 2020
Later among the works it cites.
PI-RCNN: An efficient multi-sensor 3D object detector with point-based attentive cont-conv fusion module
Xie, L.; Xiang, C.; Yu, Z.; Xu, G.; Yang, Z.; Cai, D.; and He, X. 2020 · 2020
Later among the works it cites.
3DSSD: Point-Based 3D Single Stage Object Detector
Yang, Z.; Sun, Y.; Liu, S.; and Jia, J. 2020 · 2020
Later among the works it cites.
Cascade R-CNN: High Quality Object Detection and Instance Segmentation
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Bao, W.; Xu, B.; and Chen, Z. 2020 · 2020
Cited alongside, same era.
DSGN: Deep Stereo Geometry Network for 3D Object Detection
Chen, Y.; Liu, S.; Shen, X.; and Jia, J. 2020b · 2020
Cited alongside, same era.
Object as hotspots: An anchor-free 3d object detection approach via firing of hotspots
Chen, Q.; Sun, L.; Wang, Z.; Jia, K.; and Yuille, A. 2020a
Cited in the paper.
Cai, Z.; and Vasconcelos, N. 2021 · 2021
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