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We tackle the data scarcity challenge in few-shot point cloud recognition of 3D objects by using a joint prediction from a conventional 3D model and a well-trained 2D model.
Em algorithms of gaussian mixture model and hidden markov model
Guorong Xuan, Wei Zhang, and Peiqi Chai · 2001
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Fast marching farthest point sampling
Carsten Moenning and Neil A Dodgson · 2003
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Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis
Masashi Sugiyama · 2007
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Robotic navigation and mapping with radar
Martin Adams, Martin David Adams, and Ebi Jose · 2012
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Shapenet: An information-rich 3d model repository
Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al · 2015
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Multi-view convolutional neural networks for 3d shape recognition
Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik Learned-Miller · 2015
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3d shapenets: A deep representation for volumetric shapes
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao · 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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Scenenn: A scene meshes dataset with annotations
Binh-Son Hua, Quang-Hieu Pham, Duc Thanh Nguyen, Minh-Khoi Tran, Lap-Fai Yu, and Sai-Kit Yeung · 2016
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Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
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Training region-based object detectors with online hard example mining
Abhinav Shrivastava, Abhinav Gupta, and Ross Girshick · 2016
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Scannet: Richly-annotated 3d reconstructions of indoor scenes
Angela Dai, Angel X Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner · 2017
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In defense of the triplet loss for person re-identification
Alexander Hermans, Lucas Beyer, and Bastian Leibe · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 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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Exploiting the panorama representation for convolutional neural network classification and retrieval
Konstantinos Sfikas, Theoharis Theoharis, and Ioannis Pratikakis · 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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Rotationnet: Joint object categorization and pose estimation using multiviews from unsupervised viewpoints
Asako Kanezaki, Yasuyuki Matsushita, and Yoshifumi Nishida · 2018
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Pointcnn: Convolution on x-transformed points
Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, and Baoquan Chen · 2018
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Spidercnn: Deep learning on point sets with parameterized convolutional filters
Yifan Xu, Tianqi Fan, Mingye Xu, Long Zeng, and Yu Qiao · 2018
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Pvnet: A joint convolutional network of point cloud and multi-view for 3d shape recognition
Haoxuan You, Yifan Feng, Rongrong Ji, and Yue Gao · 2018
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Convolutional networks with dense connectivity
Gao Huang, Zhuang Liu, Geoff Pleiss, Laurens Van Der Maaten, and Kilian Weinberger · 2019
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Progress of display performances: Ar, vr, qled, oled, and tft
Ho Jin Jang, Jun Yeob Lee, Jeonghun Kwak, Dukho Lee, Jae-Hyeung Park, Byoungho Lee, and Yong Young Noh · 2019
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Mlvcnn: Multi-loop-view convolutional neural network for 3d shape retrieval
Jianwen Jiang, Di Bao, Ziqiang Chen, Xibin Zhao, and Yue Gao · 2019
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A-cnn: Annularly convolutional neural networks on point clouds
Artem Komarichev, Zichun Zhong, and Jing Hua · 2019
Cited alongside, same era.
Densepoint: Learning densely contextual representation for efficient point cloud processing
Yongcheng Liu, Bin Fan, Gaofeng Meng, Jiwen Lu, Shiming Xiang, and Chunhong Pan · 2019
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Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks
Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee · 2019
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Self-supervised deep learning on point clouds by reconstructing space
Jonathan Sauder and Bjarne Sievers · 2019
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Stochastic class-based hard example mining for deep metric learning
Yumin Suh, Bohyung Han, Wonsik Kim, and Kyoung Mu Lee · 2019
Cited alongside, same era.
Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data
Towards efficient graph convolutional networks for point cloud handling
Yawei Li, He Chen, Zhaopeng Cui, Radu Timofte, Marc Pollefeys, Gregory S Chirikjian, and Luc Van Gool · 2021
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Learning from 2d: Contrastive pixel-to-point knowledge transfer for 3d pretraining
Yueh-Cheng Liu, Yu-Kai Huang, Hung-Yueh Chiang, Hung-Ting Su, Zhe-Yu Liu, Chin-Tang Chen, Ching-Yu Tseng, and Winston H Hsu · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Using shape to categorize: Low-shot learning with an explicit shape bias
Stefan Stojanov, Anh Thai, and James M Rehg · 2021
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Unsupervised point cloud pre-training via occlusion completion
Hanchen Wang, Qi Liu, Xiangyu Yue, Joan Lasenby, and Matt J Kusner · 2021
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Mikaela Angelina Uy, Quang-Hieu Pham, Binh-Son Hua, Thanh Nguyen, and Sai-Kit Yeung · 2019
Cited alongside, same era.
Dynamic graph cnn for learning on point clouds
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon · 2019
Cited alongside, same era.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Rscnn: A cnn-based method to enhance low-light remote-sensing images
Linshu Hu, Mengjiao Qin, Feng Zhang, Zhenhong Du, and Renyi Liu · 2020
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Supervised contrastive learning
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan · 2020
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Dividemix: Learning with noisy labels as semi-supervised learning
Junnan Li, Richard Socher, and Steven CH Hoi · 2020
Cited alongside, same era.
Point transformer
Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip HS Torr, and Vladlen Koltun · 2021
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Adaptive graph convolution for point cloud analysis
Haoran Zhou, Yidan Feng, Mingsheng Fang, Mingqiang Wei, Jing Qin, and Tong Lu · 2021
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Machine learning
Zhi-Hua Zhou · 2021
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Crosspoint: Self-supervised cross-modal contrastive learning for 3d point cloud understanding
Mohamed Afham, Isuru Dissanayake, Dinithi Dissanayake, Amaya Dharmasiri, Kanchana Thilakarathna, and Ranga Rodrigo · 2022
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Calibrated ensembles can mitigate accuracy tradeoffs under distribution shift
Ananya Kumar, Tengyu Ma, Percy Liang, and Aditi Raghunathan · 2022
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Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi · 2022
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Rethinking network design and local geometry in point cloud: A simple residual mlp framework
Xu Ma, Can Qin, Haoxuan You, Haoxi Ran, and Yun Fu · 2022
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Masked autoencoders for point cloud self-supervised learning
Yatian Pang, Wenxiao Wang, Francis EH Tay, Wei Liu, Yonghong Tian, and Li Yuan · 2022
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Pointnext: Revisiting pointnet++ with improved training and scaling strategies
Guocheng Qian, Yuchen Li, Houwen Peng, Jinjie Mai, Hasan Abed Al Kader Hammoud, Mohamed Elhoseiny, and Bernard Ghanem · 2022
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Benchmarking robustness of 3d point cloud recognition against common corruptions
Jiachen Sun, Qingzhao Zhang, Bhavya Kailkhura, Zhiding Yu, Chaowei Xiao, and Z Morley Mao · 2022
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Invariant feature learning for generalized long-tailed classification
Kaihua Tang, Mingyuan Tao, Jiaxin Qi, Zhenguang Liu, and Hanwang Zhang · 2022
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P2p: Tuning pre-trained image models for point cloud analysis with point-to-pixel prompting
Ziyi Wang, Xumin Yu, Yongming Rao, Jie Zhou, and Jiwen Lu · 2022
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Image2point: 3d point-cloud understanding with 2d image pretrained models
Chenfeng Xu, Shijia Yang, Tomer Galanti, Bichen Wu, Xiangyu Yue, Bohan Zhai, Wei Zhan, Peter Vajda, Kurt Keutzer, and Masayoshi Tomizuka · 2022
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Let images give you more: Point cloud cross-modal training for shape analysis
Xu Yan, Heshen Zhan, Chaoda Zheng, Jiantao Gao, Ruimao Zhang, Shuguang Cui, and Zhen Li · 2022
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Identifying hard noise in long-tailed sample distribution
Xuanyu Yi, Kaihua Tang, Xian-Sheng Hua, Joo-Hwee Lim, and Hanwang Zhang · 2022
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Point-bert: Pre-training 3d point cloud transformers with masked point modeling
Xumin Yu, Lulu Tang, Yongming Rao, Tiejun Huang, Jie Zhou, and Jiwen Lu · 2022
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Pointclip: Point cloud understanding by clip
Renrui Zhang, Ziyu Guo, Wei Zhang, Kunchang Li, Xupeng Miao, Bin Cui, Yu Qiao, Peng Gao, and Hongsheng Li · 2022
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Trosd: A new rgb-d dataset for transparent and reflective object segmentation in practice
Tianyu Sun, Guodong Zhang, Wenming Yang, Jing-Hao Xue, and Guijin Wang · 2023
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Omniobject3d: Large-vocabulary 3d object dataset for realistic perception, reconstruction and generation
Tong Wu, Jiarui Zhang, Xiao Fu, Yuxin Wang, Jiawei Ren, Liang Pan, Wayne Wu, Lei Yang, Jiaqi Wang, Chen Qian, et al · 2023
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Mvimgnet: A large-scale dataset of multi-view images
Xianggang Yu, Mutian Xu, Yidan Zhang, Haolin Liu, Chongjie Ye, Yushuang Wu, Zizheng Yan, Chenming Zhu, Zhangyang Xiong, Tianyou Liang, et al · 2023
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