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It is counter-intuitive that multi-modality methods based on point cloud and images perform only marginally better or sometimes worse than approaches that solely use point cloud.
Are we ready for autonomous driving? the kitti vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
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Vision meets robotics: The KITTI dataset
Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun · 2013
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A multi-sensor fusion system for moving object detection and tracking in urban driving environments
Hyunggi Cho, Young-Woo Seo, B. V. K. Vijaya Kumar, and Ragunathan Rajkumar · 2014
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Microsoft COCO: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Spatial transformer networks
Max Jaderberg, Karen Simonyan, Andrew Zisserman, and Koray Kavukcuoglu · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Faster R-CNN: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Multi-view 3D object detection network for autonomous driving
Xiaozhi Chen, Huimin Ma, Ji Wan, Bo Li, and Tian Xia · 2017
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Improved regularization of convolutional neural networks with cutout
Terrance Devries and Graham W. Taylor · 2017
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Cut, paste and learn: Surprisingly easy synthesis for instance detection
Debidatta Dwibedi, Ishan Misra, and Martial Hebert · 2017
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Synthesizing training data for object detection in indoor scenes
Georgios Georgakis, Arsalan Mousavian, Alexander C. Berg, and Jana Kosecka · 2017
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Mask R-CNN
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross B. Girshick · 2017
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Improving generalization performance by switching from adam to SGD
Nitish Shirish Keskar and Richard Socher · 2017
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Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross B. Girshick, Kaiming He, Bharath Hariharan, and Serge J. Belongie · 2017
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SGDR: stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
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PointNet: Deep learning on point sets for 3D classification and segmentation
Charles Ruizhongtai Qi, Hao Su, Kaichun Mo, and Leonidas J. Guibas · 2017
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PointNet++: Deep hierarchical feature learning on point sets in a metric space
Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J. Guibas · 2017
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Cascade R-CNN: Delving into high quality object detection
Zhaowei Cai and Nuno Vasconcelos · 2018
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3d object proposals using stereo imagery for accurate object class detection
Xiaozhi Chen, Kaustav Kundu, Yukun Zhu, Huimin Ma, Sanja Fidler, and Raquel Urtasun · 2018
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Modeling visual context is key to augmenting object detection datasets
Nikita Dvornik, Julien Mairal, and Cordelia Schmid · 2018
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Joint 3D proposal generation and object detection from view aggregation
Jason Ku, Melissa Mozifian, Jungwook Lee, Ali Harakeh, and Steven L. Waslander · 2018
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Deep continuous fusion for multi-sensor 3d object detection
Ming Liang, Bin Yang, Shenlong Wang, and Raquel Urtasun · 2018
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Path aggregation network for instance segmentation
Shu Liu, Lu Qi, Haifang Qin, Jianping Shi, and Jiaya Jia · 2018
MVXNet: Multimodal voxelnet for 3D object detection
Vishwanath A. Sindagi, Yin Zhou, and Oncel Tuzel · 2019
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Pseudo-lidar from visual depth estimation: Bridging the gap in 3D object detection for autonomous driving
Yan Wang, Wei-Lun Chao, Divyansh Garg, Bharath Hariharan, Mark E. Campbell, and Kilian Q. Weinberger · 2019
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Frustum ConvNet: Sliding frustums to aggregate local point-wise features for amodal 3D object detection
Zhixin Wang and Kui Jia · 2019
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Detectron2
Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick · 2019
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STD: Sparse-to-dense 3D object detector for point cloud
Zetong Yang, Yanan Sun, Shu Liu, Xiaoyong Shen, and Jiaya Jia · 2019
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CutMix: Regularization strategy to train strong classifiers with localizable features
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Cited alongside, same era.
Frustum PointNets for 3D object detection from RGB-D data
Charles R. Qi, Wei Liu, Chenxia Wu, Hao Su, and Leonidas J. Guibas · 2018
Cited alongside, same era.
SECOND: Sparsely embedded convolutional detection
Yan Yan, Yuxing Mao, and Bo Li · 2018
Cited alongside, same era.
VoxelNet: End-to-end learning for point cloud based 3D object detection
Yin Zhou and Oncel Tuzel · 2018
Cited alongside, same era.
nuScenes: A multimodal dataset for autonomous driving
Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom · 2019
Cited alongside, same era.
Hybrid task cascade for instance segmentation
Kai Chen, Jiangmiao Pang, Jiaqi Wang, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jianping Shi, Wanli Ouyang, Chen Change Loy, and Dahua Lin · 2019
Cited alongside, same era.
MMDetection: Open mmlab detection toolbox and benchmark
Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, Zheng Zhang, Dazhi Cheng, Chenchen Zhu, Tianheng Cheng, Qijie Zhao, Buyu Li, Xin Lu, Rui Zhu, Yue Wu, Jifeng Dai, Jingdong Wang, Jianping Shi, Wanli Ouyang, Chen Change Loy, and Dahua Lin · 2019
Cited alongside, same era.
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
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FreeAnchor: Learning to match anchors for visual object detection
Xiaosong Zhang, Fang Wan, Chang Liu, Rongrong Ji, and Qixiang Ye · 2019
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Class-balanced grouping and sampling for point cloud 3D object detection
Benjin Zhu, Zhengkai Jiang, Xiangxin Zhou, Zeming Li, and Gang Yu · 2019
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Learning data augmentation strategies for object detection
Barret Zoph, Ekin D. Cubuk, Golnaz Ghiasi, Tsung-Yi Lin, Jonathon Shlens, and Quoc V. Le · 2019
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TANet: Robust 3D object detection from point clouds with triple attention
Zhe Liu, Xin Zhao, Tengteng Huang, Ruolan Hu, Yu Zhou, and Xiang Bai · 2020
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Learning to evaluate perception models using planner-centric metrics
Jonah Philion, Amlan Kar, and Sanja Fidler · 2020
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ImVoteNet: Boosting 3d object detection in point clouds with image votes
Charles R. Qi, Xinlei Chen, Or Litany, and Leonidas J. Guibas · 2020
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Designing network design spaces
Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, and Piotr Dollar · 2020
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PV-RCNN: Point-voxel feature set abstraction for 3D object detection
Shaoshuai Shi, Chaoxu Guo, Li Jiang, Zhe Wang, Jianping Shi, Xiaogang Wang, and Hongsheng Li · 2020
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PointPainting: Sequential fusion for 3D object detection
Sourabh Vora, Alex H. Lang, Bassam Helou, and Oscar Beijbom · 2020
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3DSSD: Point-based 3D single stage object detector
Zetong Yang, Yanan Sun, Shu Liu, and Jiaya Jia · 2020
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Center-based 3D object detection and tracking
Tianwei Yin, Xingyi Zhou, and Philipp Krähenbühl · 2020
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3D-CVF: Generating joint camera and lidar features using cross-view spatial feature fusion for 3D object detection
Jin Hyeok Yoo, Yecheol Kim, Ji Song Kim, and Jun Won Choi · 2020
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Towards theoretically understanding why sgd generalizes better than adam in deep learning
Pan Zhou, Jiashi Feng, Chao Ma, Caiming Xiong, Steven Chu-Hong Hoi, and Weinan E · 2020
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