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In LiDAR-based 3D object detection for autonomous driving, the ratio of the object size to input scene size is significantly smaller compared to 2D detection cases.
Microsoft COCO: Common Objects in Context
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Attention-based Models for Speech Recognition
Jan Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk, Kyunghyun Cho, and Yoshua Bengio · 2015
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Vehicle Detection from 3D Lidar Using Fully Convolutional Network
Bo Li, Tianlei Zhang, and Tian Xia · 2016
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SSD: Single Shot Multibox Detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg · 2016
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Beyond Skip Connections: Top-down Modulation for Object Detection
Abhinav Shrivastava, Rahul Sukthankar, Jitendra Malik, and Abhinav Gupta · 2016
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Multi-Scale Context Aggregation by Dilated Convolutions
Fisher Yu and Vladlen Koltun · 2016
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DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille · 2017
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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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DSSD: Deconvolutional Single Shot Detector
Cheng-Yang Fu, Wei Liu, Ananth Ranga, Ambrish Tyagi, and Alexander C Berg · 2017
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Submanifold Sparse Convolutional Networks
Benjamin Graham and Laurens van der Maaten · 2017
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Feature Pyramid Networks for Object Detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 2017
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Focal Loss for Dense Object Detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Charles R 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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Attention Is All You Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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CornerNet: Detecting Objects as Paired Keypoints
Hei Law and Jia Deng · 2018
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Frustum PointNets for 3D Object Detection from RGB-D Data
Charles R Qi, Wei Liu, Chenxia Wu, Hao Su, and Leonidas J Guibas · 2018
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An Analysis of Scale Invariance in Object Detection - SNIP
Bharat Singh and Larry S Davis · 2018
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SNIPER: Efficient Multi-Scale Training
Bharat Singh, Mahyar Najibi, and Larry S Davis · 2018
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DOTA: A Large-scale Dataset for Object Detection in Aerial Images
Gui-Song Xia, Xiang Bai, Jian Ding, Zhen Zhu, Serge Belongie, Jiebo Luo, Mihai Datcu, Marcello Pelillo, and Liangpei Zhang · 2018
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SECOND: Sparsely Embedded Convolutional Detection
Yan Yan, Yuxing Mao, and Bo Li · 2018
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VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
Yin Zhou and Oncel Tuzel · 2018
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Visdrone-det2018: The Vision Meets Drone Object Detection in Image Challenge Results
Pengfei Zhu, Longyin Wen, Dawei Du, Xiao Bian, Haibin Ling, Qinghua Hu, Qinqin Nie, Hao Cheng, Chenfeng Liu, Xiaoyu Liu, et al · 2018
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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, et al · 2019
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Fast Point R-CNN
Yilun Chen, Shu Liu, Xiaoyong Shen, and Jiaya Jia · 2019
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Augmentation for Small Object Detection
Mate Kisantal, Zbigniew Wojna, Jakub Murawski, Jacek Naruniec, and Kyunghyun Cho · 2019
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PointPillars: Fast Encoders for Object Detection from Point Clouds
Alex H Lang, Sourabh Vora, Holger Caesar, Lubing Zhou, Jiong Yang, and Oscar Beijbom · 2019
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Scale-Aware Trident Networks for Object Detection
Yanghao Li, Yuntao Chen, Naiyan Wang, and Zhaoxiang Zhang · 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 · 2020
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To the Point: Efficient 3D Object Detection in the Range Image With Graph Convolution Kernels
Yuning Chai, Pei Sun, Jiquan Ngiam, Weiyue Wang, Benjamin Caine, Vijay Vasudevan, Xiao Zhang, and Dragomir Anguelov · 2021
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Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection
Jiajun Deng, Shaoshuai Shi, Peiwei Li, Wengang Zhou, Yanyong Zhang, and Houqiang Li · 2021
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XCiT: Cross-Covariance Image Transformers
Alaaeldin El-Nouby, Hugo Touvron, Mathilde Caron, Piotr Bojanowski, Matthijs Douze, Armand Joulin, Ivan Laptev, Natalia Neverova, Gabriel Synnaeve, Jakob Verbeek, et al · 2021
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RangeDet: In Defense of Range View for LiDAR-Based 3D Object Detection
Lue Fan, Xuan Xiong, Feng Wang, Naiyan Wang, and ZhaoXiang Zhang · 2021
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LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving
Gregory P Meyer, Ankit Laddha, Eric Kee, Carlos Vallespi-Gonzalez, and Carl K Wellington · 2019
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Stand-Alone Self-Attention in Vision Models
Prajit Ramachandran, Niki Parmar, Ashish Vaswani, Irwan Bello, Anselm Levskaya, and Jon Shlens · 2019
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PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud
Shaoshuai Shi, Xiaogang Wang, and Hongsheng Li · 2019
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A Baseline for 3D Multi-object Tracking
Xinshuo Weng and Kris Kitani · 2019
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Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection
Alex Bewley, Pei Sun, Thomas Mensink, Dragomir Anguelov, and Cristian Sminchisescu · 2020
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End-to-end Object Detection with Transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
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MMDetection3D: OpenMMLab Next-generation Platform for General 3D Object Detection
MMDetection3D Contributors · 2020
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PCT: Point Cloud Transformer
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From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to-Point Decoder
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LiDAR R-CNN: An Efficient and Universal 3D Object Detector
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Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection
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Voxel Transformer for 3D Object Detection
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Voxel Transformer for 3D Object Detection
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An End-to-End Transformer Model for 3D Object Detection
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3d Object Detection with Pointformer
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Offboard 3D Object Detection from Point Cloud Sequences
Charles R Qi, Yin Zhou, Mahyar Najibi, Pei Sun, Khoa Vo, Boyang Deng, and Dragomir Anguelov · 2021
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DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification
Yongming Rao, Wenliang Zhao, Benlin Liu, Jiwen Lu, Jie Zhou, and Cho-Jui Hsieh · 2021
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Shaoshuai Shi, Li Jiang, Jiajun Deng, Zhe Wang, Chaoxu Guo, Jianping Shi, Xiaogang Wang, and Hongsheng Li · 2021
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RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection
Pei Sun, Weiyue Wang, Yuning Chai, Gamaleldin Elsayed, Alex Bewley, Xiao Zhang, Cristian Sminchisescu, and Dragomir Anguelov · 2021
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Training Data-efficient Image Transformers & Distillation through Attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
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PnP-DETR: Towards Efficient Visual Analysis with Transformers
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Rethinking and Improving Relative Position Encoding for Vision Transformer
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QueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small Object Detection
Chenhongyi Yang, Zehao Huang, and Naiyan Wang · 2021
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3D-MAN: 3D Multi-Frame Attention Network for Object Detection
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Point Transformer
Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip HS Torr, and Vladlen Koltun · 2021
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