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3D object detection is an essential task for computer vision applications in autonomous vehicles and robotics.
Bayesian Neural Networks and Density Networks
D. J. Mackay · 1995
Earlier work this paper cites.
Practical variational inference for neural networks
A. Graves · 2011
Earlier work this paper cites.
Weight Uncertainty in Neural Network
C. Blundell, J. Cornebise, K. Kavukcuoglu, and D. Wierstra · 2015
Earlier work this paper cites.
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks
J. M. Hernández-Lobato and R. Adams · 2015
Earlier work this paper cites.
Variational Dropout and the Local Parameterization Trick
D. P. Kingma, T. Salimans, and M. Welling · 2015
Earlier work this paper cites.
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal and Z. Ghahramani · 2016
Earlier work this paper cites.
Simple and Scalable Predictive Uncertainty Estimation Using Deep Ensembles
B. Lakshminarayanan, A. Pritzel, and C. Blundell · 2017
Earlier work this paper cites.
Focal Loss for Dense Object Detection
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2017
Earlier work this paper cites.
Subjective Logic: A formalism for reasoning under uncertainty
A. Jsang · 2018
Earlier work this paper cites.
Cornernet: Detecting objects as paired keypoints
H. Law and J. Deng · 2018
Earlier work this paper cites.
Predictive Uncertainty Estimation via Prior Networks
A. Malinin and M. Gales · 2018
Earlier work this paper cites.
Evidential deep learning to quantify classification uncertainty
M. Sensoy, L. Kaplan, and M. Kandemir · 2018
Earlier work this paper cites.
Voxelnet: End-to-end learning for point cloud based 3d object detection
Y. Zhou and O. Tuzel · 2018
Earlier work this paper cites.
Pointpillars: Fast encoders for object detection from point clouds
A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom · 2019
Earlier work this paper cites.
Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Dhift
Y. Ovadia, E. Fertig, J. Ren, Z. Nado, D. Sculley, S. Nowozin, J. Dillon, B. Lakshminarayanan, and J. Snoek · 2019
Earlier work this paper cites.
Sampling-Free Epistemic Uncertainty Estimation Using Approximated Variance Propagation
J. Postels, F. Ferroni, H. Coskun, N. Navab, and F. Tombari · 2019
Earlier work this paper cites.
Objects as Points
X. Zhou, D. Wang, and P. Krähenbühl · 2019
Earlier work this paper cites.
Uncertainty estimation using a single deep deterministic neural network
L. Van Amersfoort, J. A. Smith, L. A. Teh, Y. W. Teh, and Y. Gal · 2020
Earlier work this paper cites.
Deep evidential regression
A. Amini, W. Schwarting, A. Soleimany, and D. Rus · 2020
Cited alongside, same era.
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning
A. Ashukha, A. Lyzhov, D. Molchanov, and D. Vetrov · 2020
Cited alongside, same era.
nuscenes: A multimodal dataset for autonomous driving
H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom · 2020
Cited alongside, same era.
End-to-end object detection with transformers
N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko · 2020
Cited alongside, same era.
MMDetection3D: OpenMMLab next-generation platform for general 3D object detection
MMDetection3D Contributors · 2020
Cited alongside, same era.
Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision
F. K. Gustafsson, M. Danelljan, and T. B. Schön · 2020
Prior and posterior networks: A survey on evidential deep learning methods for uncertainty estimation
D. Ulmer, C. Hardmeier, and J. Frellsen · 2021
Later among the works it cites.
Pointaugmenting: Cross-modal augmentation for 3d object detection
C. Wang, C. Ma, M. Zhu, and X. Yang · 2021
Later among the works it cites.
Center-based 3d object detection and tracking
T. Yin, X. Zhou, and P. Krahenbuhl · 2021
Later among the works it cites.
Deformable detr: Deformable transformers for end-to-end object detection
X. Zhu, W. Su, L. Lu, B. Li, X. Wang, and J. Dai · 2021
Later among the works it cites.
Transfusion: Robust lidar-camera fusion for 3d object detection with transformers
X. Bai, Z. Hu, X. Zhu, Q. Huang, Y. Chen, H. Fu, and C.-L. Tai · 2022
Later among the works it cites.
PartAL: Efficient Partial Active Learning in Multi-Task Visual Settings
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Depth uncertainty in neural networks
J. Allingham J. Antoran and J. M. Hernandez-Lobato · 2020
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Bev-seg: Bird’s eye view semantic segmentation using geometry and semantic point cloud
M. H. Ng, K. Radia, J. Chen, D. Wang, I. Gog, and J. E. Gonzalez · 2020
Cited alongside, same era.
Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d
J. Philion and S. Fidler · 2020
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Scalability in perception for autonomous driving: Waymo open dataset
P. Sun, H. Kretzschmar, X. Dotiwalla, A. Chouard, V. Patnaik, P. Tsui, J. Guo, Y. Zhou, Y. Chai, B. Caine, et al · 2020
Cited alongside, same era.
Train in germany, test in the usa: Making 3d object detectors generalize
Y. Wang, X. Chen, Y. You, L. E. Li, B. Hariharan, M. Campbell, K. Q. Weinberger, and W.-L. Chao · 2020
Cited alongside, same era.
Batchensemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning
Y. Wen, D. Tran, and J. Ba · 2020
Cited alongside, same era.
N. Durasov, N. Dorndorf, and P. Fua · 2022
Later among the works it cites.
Not all labels are equal: Rationalizing the labeling costs for training object detection
I. Elezi, Z. Yu, A. Anandkumar, L. Leal-Taixe, and J. M. Alvarez · 2022
Later among the works it cites.
Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers
Z. Li, W. Wang, H. Li, E. Xie, C. Sima, T. Lu, Y. Qiao, and J. Dai · 2022
Later among the works it cites.
Bevfusion: A simple and robust lidar-camera fusion framework
T. Liang, H. Xie, K. Yu, Z. Xia, Z. Lin, Y. Wang, T. Tang, B. Wang, and Z. Tang · 2022
Later among the works it cites.
Petr: Position embedding transformation for multi-view 3d object detection
Y. Liu, T. Wang, X. Zhang, and J. Sun · 2022
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On the Practicality of Deterministic Epistemic Uncertainty
J. Postels, M. Segu, T. Sun, L. Van Gool, F. Yu, and F. Tombari · 2022
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Continual evidential deep learning for out-of-distribution detection
E. Aguilar, B. Raducanu, P. Radeva, and J. Van de Weijer · 2023
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Focalformer3d: Focusing on hard instance for 3d object detection
Y. Chen, Z. Yu, Y. Chen, S. Lan, A. Anandkumar, J. Jia, and J. M. Alvarez · 2023
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Packed ensembles for efficient uncertainty estimation
O. Laurent, A. Lafage, E. Tartaglione, G. Daniel, J.-M. Martinez, A. Bursuc, and G. Franchi · 2023
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Exploring object-centric temporal modeling for efficient multi-view 3d object detection
S. Wang, Y. Liu, T. Wang, Y. Li, and X. Zhang · 2023
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Open set action recognition via multi-label evidential learning
C. Zhao, D. Du, A. Hoogs, and C. Funk · 2023
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Beyond active learning: Leveraging the full potential of human interaction via auto-labeling, human correction, and human verification
N. Beck, K. Killamsetty, S. Kothawade, and R. Iyer · 2024
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