Fetching the paper…
Reading the bibliography…
Point cloud analysis has seen substantial advancements due to deep learning, although previous Transformer-based methods excel at modeling long-range dependencies on this task, their computational demands are substantial.
Über die stetige abbildung einer linie auf ein flächenstück
David Hilbert and David Hilbert · 1935
Earlier work this paper cites.
A computer oriented geodetic data base and a new technique in file sequencing
Guy M Morton · 1966
Earlier work this paper cites.
A convolutional learning system for object classification in 3-d lidar data
Danil Prokhorov · 2010
Earlier work this paper cites.
Voxnet: A 3d convolutional neural network for real-time object recognition
Daniel Maturana and Sebastian Scherer · 2015
Earlier work this paper cites.
3d shapenets: A deep representation for volumetric shapes
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao · 2015
Earlier work this paper cites.
3d convolutional neural networks for landing zone detection from lidar
Daniel Maturana and Sebastian Scherer · 2015
Earlier work this paper cites.
Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
Earlier work this paper cites.
A scalable active framework for region annotation in 3d shape collections
Li Yi, Vladimir G Kim, Duygu Ceylan, I-Chao Shen, Mengyan Yan, Hao Su, Cewu Lu, Qixing Huang, Alla Sheffer, and Leonidas Guibas · 2016
Earlier work this paper cites.
Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
Earlier work this paper cites.
Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
Earlier work this paper cites.
Pointcnn: Convolution on x-transformed points
Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, and Baoquan Chen · 2018
Earlier work this paper cites.
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
Earlier work this paper cites.
Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data
Mikaela Angelina Uy, Quang-Hieu Pham, Binh-Son Hua, Thanh Nguyen, and Sai-Kit Yeung · 2019
Earlier work this paper cites.
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
Earlier work this paper cites.
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
Cited alongside, same era.
Global-local bidirectional reasoning for unsupervised representation learning of 3d point clouds
Yongming Rao, Jiwen Lu, and Jie Zhou · 2020
Cited alongside, same era.
Combining recurrent, convolutional, and continuous-time models with linear state space layers
Albert Gu, Isys Johnson, Karan Goel, Khaled Saab, Tri Dao, Atri Rudra, and Christopher Ré · 2021
Cited alongside, same era.
Efficiently modeling long sequences with structured state spaces
Albert Gu, Karan Goel, and Christopher Ré · 2021
Cited alongside, same era.
Transformer3d-det: Improving 3d object detection by vote refinement
Lichen Zhao, Jinyang Guo, Dong Xu, and Lu Sheng · 2021
Pointnext: Revisiting pointnet++ with improved training and scaling strategies
Guocheng Qian, Yuchen Li, Houwen Peng, Jinjie Mai, Hasan Hammoud, Mohamed Elhoseiny, and Bernard Ghanem · 2022
Later among the works it cites.
Mamba: Linear-time sequence modeling with selective state spaces
Albert Gu and Tri Dao · 2023
Later among the works it cites.
Domain adaptive sampling for cross-domain point cloud recognition
Zicheng Wang, Wen Li, and Dong Xu · 2023
Later among the works it cites.
Octformer: Octree-based transformers for 3d point clouds
Peng-Shuai Wang · 2023
Later among the works it cites.
Attention-based point cloud edge sampling
Chengzhi Wu, Junwei Zheng, Julius Pfrommer, and Jürgen Beyerer · 2023
Later among the works it cites.
Can mamba learn how to learn? a comparative study on in-context learning tasks
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Point transformer
Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip HS Torr, and Vladlen Koltun · 2021
Cited alongside, same era.
Pct: Point cloud transformer
Meng-Hao Guo, Jun-Xiong Cai, Zheng-Ning Liu, Tai-Jiang Mu, Ralph R Martin, and Shi-Min Hu · 2021
Cited alongside, same era.
Mvtn: Multi-view transformation network for 3d shape recognition
Abdullah Hamdi, Silvio Giancola, and Bernard Ghanem · 2021
Cited alongside, same era.
3d-sps: Single-stage 3d visual grounding via referred point progressive selection
Junyu Luo, Jiahui Fu, Xianghao Kong, Chen Gao, Haibing Ren, Hao Shen, Huaxia Xia, and Si Liu · 2022
Cited alongside, same era.
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
Cited alongside, same era.
Masked autoencoders for point cloud self-supervised learning
Yatian Pang, Wenxiao Wang, Francis EH Tay, Wei Liu, Yonghong Tian, and Li Yuan · 2022
Cited alongside, same era.
Simplified state space layers for sequence modeling
Jimmy TH Smith, Andrew Warrington, and Scott W Linderman · 2022
Cited alongside, same era.
Jongho Park, Jaeseung Park, Zheyang Xiong, Nayoung Lee, Jaewoong Cho, Samet Oymak, Kangwook Lee, and Dimitris Papailiopoulos · 2024
Closest in time.
Densemamba: State space models with dense hidden connection for efficient large language models
Wei He, Kai Han, Yehui Tang, Chengcheng Wang, Yujie Yang, Tianyu Guo, and Yunhe Wang · 2024
Closest in time.
Vmamba: Visual state space model
Yue Liu, Yunjie Tian, Yuzhong Zhao, Hongtian Yu, Lingxi Xie, Yaowei Wang, Qixiang Ye, and Yunfan Liu · 2024
Closest in time.
Vision mamba: Efficient visual representation learning with bidirectional state space model
Lianghui Zhu, Bencheng Liao, Qian Zhang, Xinlong Wang, Wenyu Liu, and Xinggang Wang · 2024
Closest in time.
Pointmamba: A simple state space model for point cloud analysis
Dingkang Liang, Xin Zhou, Xinyu Wang, Xingkui Zhu, Wei Xu, Zhikang Zou, Xiaoqing Ye, and Xiang Bai · 2024
Closest in time.
Point could mamba: Point cloud learning via state space model
Tao Zhang, Xiangtai Li, Haobo Yuan, Shunping Ji, and Shuicheng Yan · 2024
Closest in time.
A survey on vision mamba: Models, applications and challenges
Rui Xu, Shu Yang, Yihui Wang, Bo Du, and Hao Chen · 2024
Closest in time.
U-mamba: Enhancing long-range dependency for biomedical image segmentation
Jun Ma, Feifei Li, and Bo Wang · 2024
Closest in time.
Jamba: A hybrid transformer-mamba language model
Opher Lieber, Barak Lenz, Hofit Bata, Gal Cohen, Jhonathan Osin, Itay Dalmedigos, Erez Safahi, Shaked Meirom, Yonatan Belinkov, Shai Shalev-Shwartz, et al · 2024
Closest in time.
Can mamba learn how to learn? a comparative study on in-context learning tasks
Jongho Park, Jaeseung Park, Zheyang Xiong, Nayoung Lee, Jaewoong Cho, Samet Oymak, Kangwook Lee, and Dimitris Papailiopoulos · 2024
Closest in time.