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We present a robust real-time LiDAR 3D object detector that leverages heteroscedastic aleatoric uncertainties to significantly improve its detection performance.
D. J. C. MacKay, “A practical Bayesian framework for backpropagation networks,” Neural Computation , vol. 4, no. 3, pp. 448–472, 1992
1992
Earlier work this paper cites.
G. E. Hinton and D. Van Camp, “Keeping the neural networks simple by minimizing the description length of the weights,” in Proc. 6th Annu. Conf. Computational Learning Theory . ACM, 1993, pp. 5–13
1993
Earlier work this paper cites.
A. Graves, “Practical variational inference for neural networks,” in Advances in Neural Information Processing Systems , 2011, pp. 2348–2356
2011
Earlier work this paper cites.
A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the KITTI vision benchmark suite,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2012
2012
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” in Advances in Neural Information Processing Systems , 2015, pp. 91–99
2015
Earlier work this paper cites.
B. Li, T. Zhang, and T. Xia, “Vehicle detection from 3d lidar using fully convolutional network,” in Proc. Robotics: Science and Systems , Jun. 2016
2016
Earlier work this paper cites.
Y. Gal, “Uncertainty in deep learning,” Ph.D. dissertation, University of Cambridge, 2016
2016
Earlier work this paper cites.
T. Kim and J. Ghosh, “Robust detection of non-motorized road users using deep learning on optical and lidar data,” in IEEE 19th Int. Conf. Intelligent Transportation Systems , 2016, pp. 271–276
2016
Earlier work this paper cites.
J. Schlosser, C. K. Chow, and Z. Kira, “Fusing lidar and images for pedestrian detection using convolutional neural networks,” in IEEE Int. Conf. Robotics and Automation , 2016, pp. 2198–2205
2016
Earlier work this paper cites.
M. Kampffmeyer, A.-B. Salberg, and R. Jenssen, “Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks,” in IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2016, pp. 680–688
2016
Earlier work this paper cites.
A. Kendall and R. Cipolla, “Modelling uncertainty in deep learning for camera relocalization,” in IEEE Int. Conf. Robotics and Automation , May 2016, pp. 4762–4769
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
B. Li, “3d fully convolutional network for vehicle detection in point cloud,” in IEEE/RSJ Int. Conf. Intelligent Robots and Systems , 2017, pp. 1513–1518
2017
Cited alongside, same era.
M. Engelcke, D. Rao, D. Z. Wang, C. H. Tong, and I. Posner, “Vote3Deep: Fast object detection in 3d point clouds using efficient convolutional neural networks,” in IEEE Int. Conf. Robotics and Automation , 2017, pp. 1355–1361
2017
Cited alongside, same era.
X. Chen, H. Ma, J. Wan, B. Li, and T. Xia, “Multi-view 3d object detection network for autonomous driving,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2017, pp. 6526–6534
2017
Cited alongside, same era.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “PointNet: Deep learning on point sets for 3d classification and segmentation,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition , Jul. 2017, pp. 77–85
2017
Cited alongside, same era.
D. Xu, D. Anguelov, and A. Jain, “PointFusion: Deep sensor fusion for 3d bounding box estimation,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2018
2018
Closest in time.
C. R. Qi, W. Liu, C. Wu, H. Su, and L. J. Guibas, “Frustum PointNets for 3d object detection from RGB-D data,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2018
2018
Closest in time.
2018
Closest in time.
A. Pfeuffer and K. Dietmayer, “Optimal sensor data fusion architecture for object detection in adverse weather conditions,” in Proceedings of International Conference on Information Fusion , 2018, pp. 2592 – 2599
2018
Closest in time.
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L. Caltagirone, S. Scheidegger, L. Svensson, and M. Wahde, “Fast lidar-based road detection using fully convolutional neural networks,” in IEEE Intelligent Vehicles Symp. , 2017, pp. 1019–1024
2017
Cited alongside, same era.
A. Kendall and Y. Gal, “What uncertainties do we need in Bayesian deep learning for computer vision?” in Advances in Neural Information Processing Systems , 2017, pp. 5574–5584
2017
Cited alongside, same era.
Y. Gal, R. Islam, and Z. Ghahramani, “Deep bayesian active learning with image data,” in International Conference on Machine Learning , 2017, pp. 1183–1192
2017
Cited alongside, same era.
A. Kendall, V. Badrinarayanan, and R. Cipolla, “Bayesian SegNet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding,” in Proc. British Machine Vision Conf. , 2017
2017
Cited alongside, same era.
T. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in Proc. IEEE International Conference on Computer Vision (ICCV) , Oct 2017, pp. 2999–3007
2017
Cited alongside, same era.
“Preliminary report highway: Hwy18mh010,” National Transportation Safety Board (NTSB), Tech. Rep., 05 2018
2018
Cited alongside, same era.
Y. Zhou and O. Tuzel, “VoxelNet: End-to-end learning for point cloud based 3d object detection,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2018
2018
Cited alongside, same era.
J. Ku, M. Mozifian, J. Lee, A. Harakeh, and S. Waslander, “Joint 3d proposal generation and object detection from view aggregation,” in IEEE/RSJ Int. Conf. Intelligent Robots and Systems , Oct. 2018, pp. 1–8
2018
Cited alongside, same era.
B. Wu, A. Wan, X. Yue, and K. Keutzer, “SqueezeSeg: Convolutional neural nets with recurrent CRF for real-time road-object segmentation from 3d lidar point cloud,” in IEEE Int. Conf. Robotics and Automation , May 2018, pp. 1887–1893
2018
Closest in time.
D. Feng, L. Rosenbaum, and K. Dietmayer, “Towards safe autonomous driving: Capture uncertainty in the deep neural network for lidar 3d vehicle detection,” in 21st Int. Conf. Intelligent Transportation Systems , Nov. 2018, pp. 3266–3273
2018
Closest in time.
B. Yang, W. Luo, and R. Urtasun, “PIXOR: Real-time 3d object detection from point clouds,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2018, pp. 7652–7660
2018
Closest in time.
W. H. Beluch, T. Genewein, A. Nürnberger, and J. M. Köhler, “The power of ensembles for active learning in image classification,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition , Jun. 2018
2018
Closest in time.
D. Miller, L. Nicholson, F. Dayoub, and N. Sünderhauf, “Dropout sampling for robust object detection in open-set conditions,” in IEEE Int. Conf. Robotics and Automation , 2018
2018
Closest in time.
A. Kendall, Y. Gal, and R. Cipolla, “Multi-task learning using uncertainty to weigh losses for scene geometry and semantics,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2018
2018
Closest in time.
E. Ilg, O. Ciçek, S. Galesso, A. Klein, O. Makansi, F. Hutter, and T. Brox, “Uncertainty estimates and multi-hypotheses networks for optical flow,” in European Conference on Computer Vision (ECCV) , 2018
2018
Closest in time.