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3D object detection received increasing attention in autonomous driving recently.
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A.; Lenz, P.; and Urtasun, R. 2012 · 2012
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Group Equivariant Convolutional Networks
Cohen, T.; and Welling, M. 2016 · 2016
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TI-POOLING: Transformation-Invariant Pooling for Feature Learning in Convolutional Neural Networks
Laptev, D.; Savinov, N.; Buhmann, J. M.; and Pollefeys, M. 2016 · 2016
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Multi-view 3d object detection network for autonomous driving
Chen, X.; Ma, H.; Wan, J.; Li, B.; and Xia, T. 2017 · 2017
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Birdnet: a 3d object detection framework from lidar information
Beltrán, J.; Guindel, C.; Moreno, F. M.; Cruzado, D.; Garcia, F.; and De La Escalera, A. 2018 · 2018
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Spherical CNNs
Cohen, T.; Geiger, M.; Köhler, J.; and Welling, M. 2018 · 2018
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3D Semantic Segmentation with Submanifold Sparse Convolutional Networks
Graham, B.; Engelcke, M.; and van der Maaten, L. 2018 · 2018
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Frustum PointNets for 3D Object Detection from RGB-D Data
Qi, C.; Liu, W.; Wu, C.; Su, H.; and Guibas, L. J. 2018 · 2018
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3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data
Weiler, M.; Geiger, M.; Welling, M.; Boomsma, W.; and Cohen, T. 2018 · 2018
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Learning Steerable Filters for Rotation Equivariant CNNs
Weiler, M.; Hamprecht, F. A.; and Storath, M. 2018 · 2018
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SECOND: Sparsely Embedded Convolutional Detection
Yan, Y.; Mao, Y.; and Li, B. 2018 · 2018
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Voxelnet: End-to-end learning for point cloud based 3d object detection
Zhou, Y.; and Tuzel, O. 2018 · 2018
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Pointpillars: Fast encoders for object detection from point clouds
Lang, A. H.; Vora, S.; Caesar, H.; Zhou, L.; Yang, J.; and Beijbom, O. 2019 · 2019
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PointRCNN: 3D Object Proposal Generation and Detection From Point Cloud
Shi, S.; Wang, X.; and Li, H. 2019 · 2019
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Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal
Wang, Z.; and Jia, K. 2019 · 2019
Cited alongside, same era.
General E(2)-Equivariant Steerable CNNs
Weiler, M.; and Cesa, G. 2019 · 2019
Cited alongside, same era.
STD: Sparse-to-Dense 3D Object Detector for Point Cloud
Yang, Z.; Sun, Y.; Liu, S.; Shen, X.; and Jia, J. 2019 · 2019
Cited alongside, same era.
Structure aware single-stage 3d object detection from point cloud
He, C.; Zeng, H.; Huang, J.; Hua, X.-S.; and Zhang, L. 2020 · 2020
Cited alongside, same era.
EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection
Huang, T.; Liu, Z.; Chen, X.; and Bai, X. 2020 · 2020
Cited alongside, same era.
CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection
Pang, S.; Morris, D. D.; and Radha, H. 2020 · 2020
PENet: Towards Precise and Efficient Image Guided Depth Completion
Hu, M.; Wang, S.; Li, B.; Ning, S.; Fan, L.; and Gong, X. 2021 · 2021
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Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and Tracking of Object Poses in 3D Space
Lin, J.; Li, H.; Chen, K.; Lu, J.; and Jia, K. 2021 · 2021
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EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detection
Liu, Z.; Huang, T.; Li, B.; Chen, X.; Wang, X.; and Bai, X. 2021 · 2021
Later among the works it cites.
Better Aggregation in Test-Time Augmentation
Shanmugam, D.; Blalock, D. W.; Balakrishnan, G.; and Guttag, J. V. 2021 · 2021
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Improving 3D Object Detection with Channel-wise Transformer
Sheng, H.; Cai, S.; Liu, Y.; Deng, B.; Huang, J.; Hua, X.; and Zhao, M.-J. 2021 · 2021
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Multimodal Virtual Point 3D Detection
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Cited alongside, same era.
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
Cited alongside, same era.
Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud
Shi, W.; and Rajkumar, R. R. 2020 · 2020
Cited alongside, same era.
Scalability in perception for autonomous driving: Waymo open dataset
Sun, P.; Kretzschmar, H.; Dotiwalla, X.; and et, a. 2020 · 2020
Cited alongside, same era.
3dssd: Point-based 3d single stage object detector
Yang, Z.; Sun, Y.; Liu, S.; and Jia, J. 2020 · 2020
Cited alongside, same era.
Equivariant Point Network for 3D Point Cloud Analysis
Chen, H.; Liu, S.; Chen, W.; and Li, H. 2021 · 2021
Cited alongside, same era.
Part-Aware Data Augmentation for 3D Object Detection in Point Cloud*
Choi, J.; Song, Y.; and Kwak, N. 2021 · 2021
Cited alongside, same era.
Yin, T.; Zhou, X.; and Krähenbühl, P. 2021 · 2021
Later among the works it cites.
SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud
Zheng, W.; Tang, W.; Jiang, L.; and Fu, C.-W. 2021 · 2021
Later among the works it cites.
SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection
Chen, C.; Chen, Z.; Zhang, J.; and Tao, D. 2022 · 2022
Closest in time.
Point Density-Aware Voxels for LiDAR 3D Object Detection
Hu, J. S. K.; Kuai, T.; and Waslander, S. L. 2022 · 2022
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PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection
Shi, S.; Jiang, L.; Deng, J.; Wang, Z.; Guo, C.; Shi, J.; Wang, X.; and Li, H. 2022 · 2022
Closest in time.
Behind the Curtain: Learning Occluded Shapes for 3D Object Detection
Xu, Q.; Zhong, Y.; and Neumann, U. 2022 · 2022
Closest in time.
Rotationally Equivariant 3D Object Detection
Yu, H.-X.; Wu, J.; and Yi, L. 2022 · 2022
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Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds
Zhang, Y.; Hu, Q.; Xu, G.; Ma, Y.; Wan, J.-H.; and Guo, Y. 2022 · 2022
Closest in time.