Fetching the paper…
Reading the bibliography…
We present a Non-parametric Network for 3D point cloud analysis, Point-NN, which consists of purely non-learnable components: farthest point sampling (FPS), k-nearest neighbors (k-NN), and pooling operations, with trigonometric functions.
A survey of decision tree classifier methodology
S Rasoul Safavian and David Landgrebe · 1991
Earlier work this paper cites.
An introduction to support vector machines and other kernel-based learning methods
Nello Cristianini, John Shawe-Taylor, et al · 2000
Earlier work this paper cites.
Greedy function approximation: a gradient boosting machine
Jerome H Friedman · 2001
Earlier work this paper cites.
Close-range scene segmentation and reconstruction of 3d point cloud maps for mobile manipulation in domestic environments
Radu Bogdan Rusu, Nico Blodow, Zoltan Csaba Marton, and Michael Beetz · 2009
Earlier work this paper cites.
Pedestrian detection and tracking using three-dimensional ladar data
Luis E Navarro-Serment, Christoph Mertz, and Martial Hebert · 2010
Earlier work this paper cites.
Pedestrian recognition using high-definition lidar
Kiyosumi Kidono, Takeo Miyasaka, Akihiro Watanabe, Takashi Naito, and Jun Miura · 2011
Earlier work this paper cites.
Tutorial: Point cloud library: Three-dimensional object recognition and 6 dof pose estimation
Aitor Aldoma, Zoltan-Csaba Marton, Federico Tombari, Walter Wohlkinger, Christian Potthast, Bernhard Zeisl, Radu Bogdan Rusu, Suat Gedikli, and Markus Vincze · 2012
Earlier work this paper cites.
Beyond point clouds: Scene understanding by reasoning geometry and physics
Bo Zheng, Yibiao Zhao, Joey C Yu, Katsushi Ikeuchi, and Song-Chun Zhu · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Sun rgb-d: A rgb-d scene understanding benchmark suite
Shuran Song, Samuel P Lichtenberg, and Jianxiong Xiao · 2015
Earlier work this paper cites.
Multi-view convolutional neural networks for 3d shape recognition
Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik Learned-Miller · 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.
Analysis and observations from the first amazon picking challenge
Nikolaus Correll, Kostas E Bekris, Dmitry Berenson, Oliver Brock, Albert Causo, Kris Hauser, Kei Okada, Alberto Rodriguez, Joseph M Romano, and Peter R Wurman · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling
Jiajun Wu, Chengkai Zhang, Tianfan Xue, William T Freeman, and Joshua B Tenenbaum · 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.
Multi-view 3d object detection network for autonomous driving
Xiaozhi Chen, Huimin Ma, Ji Wan, Bo Li, and Tian Xia · 2017
Earlier work this paper cites.
Scannet: Richly-annotated 3d reconstructions of indoor scenes
Angela Dai, Angel X Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner · 2017
Earlier work this paper cites.
Convolutional sequence to sequence learning
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N Dauphin · 2017
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2017
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 R Qi, Li Yi, Hao Su, and Leonidas J Guibas · 2017
Earlier work this paper cites.
Octnet: Learning deep 3d representations at high resolutions
Gernot Riegler, Ali Osman Ulusoy, and Andreas Geiger · 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.
Fast 3d point cloud segmentation using supervoxels with geometry and color for 3d scene understanding
Francesco Verdoja, Diego Thomas, and Akihiro Sugimoto · 2017
Earlier work this paper cites.
3dmfv: Three-dimensional point cloud classification in real-time using convolutional neural networks
Yizhak Ben-Shabat, Michael Lindenbaum, and Anath Fischer · 2018
Earlier work this paper cites.
3dmv: Joint 3d-multi-view prediction for 3d semantic scene segmentation
Angela Dai and Matthias Nießner · 2018
Earlier work this paper cites.
Monte carlo convolution for learning on non-uniformly sampled point clouds
Pedro Hermosilla, Tobias Ritschel, Pere-Pau Vázquez, Àlvar Vinacua, and Timo Ropinski · 2018
Earlier work this paper cites.
Large-scale point cloud semantic segmentation with superpoint graphs
Loic Landrieu and Martin Simonovsky · 2018
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.
Tangent convolutions for dense prediction in 3d
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, and Qian-Yi Zhou · 2018
Cited alongside, same era.
Rgcnn: Regularized graph cnn for point cloud segmentation
Gusi Te, Wei Hu, Amin Zheng, and Zongming Guo · 2018
Cited alongside, same era.
Spidercnn: Deep learning on point sets with parameterized convolutional filters
Yifan Xu, Tianqi Fan, Mingye Xu, Long Zeng, and Yu Qiao · 2018
Cited alongside, same era.
Foldingnet: Point cloud auto-encoder via deep grid deformation
Yaoqing Yang, Chen Feng, Yiru Shen, and Dong Tian · 2018
Cited alongside, same era.
Deep learning approach to point cloud scene understanding for automated scan to 3d reconstruction
Jingdao Chen, Zsolt Kira, and Yong K Cho · 2019
Cited alongside, same era.
Votenet: A deep learning label fusion method for multi-atlas segmentation
Zhipeng Ding, Xu Han, and Marc Niethammer · 2019
An end-to-end transformer model for 3d object detection
Ishan Misra, Rohit Girdhar, and Armand Joulin · 2021
Later among the works it cites.
Dense-resolution network for point cloud classification and segmentation
Shi Qiu, Saeed Anwar, and Nick Barnes · 2021
Later among the works it cites.
Geometric back-projection network for point cloud classification
Shi Qiu, Saeed Anwar, and Nick Barnes · 2021
Later among the works it cites.
Pnp-3d: A plug-and-play for 3d point clouds
Shi Qiu, Saeed Anwar, and Nick Barnes · 2021
Later among the works it cites.
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
Later among the works it cites.
Walk in the cloud: Learning curves for point clouds shape analysis
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Structural relational reasoning of point clouds
Yueqi Duan, Yu Zheng, Jiwen Lu, Jie Zhou, and Qi Tian · 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
Cited alongside, same era.
Relation-shape convolutional neural network for point cloud analysis
Yongcheng Liu, Bin Fan, Shiming Xiang, and Chunhong Pan · 2019
Cited alongside, same era.
Point-voxel cnn for efficient 3d deep learning
Zhijian Liu, Haotian Tang, Yujun Lin, and Song Han · 2019
Cited alongside, same era.
Vv-net: Voxel vae net with group convolutions for point cloud segmentation
Hsien-Yu Meng, Lin Gao, Yu-Kun Lai, and Dinesh Manocha · 2019
Cited alongside, same era.
6-dof graspnet: Variational grasp generation for object manipulation
Arsalan Mousavian, Clemens Eppner, and Dieter Fox · 2019
Cited alongside, same era.
Tiange Xiang, Chaoyi Zhang, Yang Song, Jianhui Yu, and Weidong Cai · 2021
Later among the works it cites.
Generative pointnet: Deep energy-based learning on unordered point sets for 3d generation, reconstruction and classification
Jianwen Xie, Yifei Xu, Zilong Zheng, Song-Chun Zhu, and Ying Nian Wu · 2021
Later among the works it cites.
Paconv: Position adaptive convolution with dynamic kernel assembling on point clouds
Mutian Xu, Runyu Ding, Hengshuang Zhao, and Xiaojuan Qi · 2021
Later among the works it cites.
Learning geometry-disentangled representation for complementary understanding of 3d object point cloud
Mutian Xu, Junhao Zhang, Zhipeng Zhou, Mingye Xu, Xiaojuan Qi, and Yu Qiao · 2021
Later among the works it cites.
Tip-adapter: Training-free clip-adapter for better vision-language modeling
Renrui Zhang, Rongyao Fang, Peng Gao, Wei Zhang, Kunchang Li, Jifeng Dai, Yu Qiao, and Hongsheng Li · 2021
Later among the works it cites.
Dspoint: Dual-scale point cloud recognition with high-frequency fusion
Renrui Zhang, Ziyao Zeng, Ziyu Guo, Xinben Gao, Kexue Fu, and Jianbo Shi · 2021
Later among the works it cites.
Point transformer
Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip HS Torr, and Vladlen Koltun · 2021
Later among the works it cites.
Pos-bert: Point cloud one-stage bert pre-training
Kexue Fu, Peng Gao, ShaoLei Liu, Renrui Zhang, Yu Qiao, and Manning Wang · 2022
Later among the works it cites.
Distillation with contrast is all you need for self-supervised point cloud representation learning
Kexue Fu, Peng Gao, Renrui Zhang, Hongsheng Li, Yu Qiao, and Manning Wang · 2022
Later among the works it cites.
Calip: Zero-shot enhancement of clip with parameter-free attention
Ziyu Guo, Renrui Zhang, Longtian Qiu, Xianzheng Ma, Xupeng Miao, Xuming He, and Bin Cui · 2022
Later among the works it cites.
Tig-bev: Multi-view bev 3d object detection via target inner-geometry learning
Peixiang Huang, Li Liu, Renrui Zhang, Song Zhang, Xinli Xu, Baichao Wang, and Guoyi Liu · 2022
Later among the works it cites.
Clip2point: Transfer clip to point cloud classification with image-depth pre-training
Tianyu Huang, Bowen Dong, Yunhan Yang, Xiaoshui Huang, Rynson WH Lau, Wanli Ouyang, and Wangmeng Zuo · 2022
Later among the works it cites.
Stratified transformer for 3d point cloud segmentation
Xin Lai, Jianhui Liu, Li Jiang, Liwei Wang, Hengshuang Zhao, Shu Liu, Xiaojuan Qi, and Jiaya Jia · 2022
Later among the works it cites.
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
Later among the works it cites.
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
Later among the works it cites.
Eda: Explicit text-decoupling and dense alignment for 3d visual and language learning
Yanmin Wu, Xinhua Cheng, Renrui Zhang, Zesen Cheng, and Jian Zhang · 2022
Later among the works it cites.
Ulip: Learning unified representation of language, image and point cloud for 3d understanding
Le Xue, Mingfei Gao, Chen Xing, Roberto Martín-Martín, Jiajun Wu, Caiming Xiong, Ran Xu, Juan Carlos Niebles, and Silvio Savarese · 2022
Later among the works it cites.
Point-m2ae: Multi-scale masked autoencoders for hierarchical point cloud pre-training
Renrui Zhang, Ziyu Guo, Peng Gao, Rongyao Fang, Bin Zhao, Dong Wang, Yu Qiao, and Hongsheng Li · 2022
Later among the works it cites.
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
Later among the works it cites.
Monodetr: Depth-aware transformer for monocular 3d object detection
Renrui Zhang, Han Qiu, Tai Wang, Xuanzhuo Xu, Ziyu Guo, Yu Qiao, Peng Gao, and Hongsheng Li · 2022
Later among the works it cites.
Learning 3d representations from 2d pre-trained models via image-to-point masked autoencoders
Renrui Zhang, Liuhui Wang, Yu Qiao, Peng Gao, and Hongsheng Li · 2022
Later among the works it cites.
Pointclip v2: Adapting clip for powerful 3d open-world learning
Xiangyang Zhu, Renrui Zhang, Bowei He, Ziyao Zeng, Shanghang Zhang, and Peng Gao · 2022
Later among the works it cites.
Pimae: Point cloud and image interactive masked autoencoders for 3d object detection
Anthony Chen, Kevin Zhang, Renrui Zhang, Zihan Wang, Yuheng Lu, Yandong Guo, and Shanghang Zhang · 2023
Closest in time.
Joint-mae: 2d-3d joint masked autoencoders for 3d point cloud pre-training
Ziyu Guo, Xianzhi Li, and Pheng Ann Heng · 2023
Closest in time.
Nearest neighbors meet deep neural networks for point cloud analysis
Renrui Zhang, Liuhui Wang, Ziyu Guo, and Jianbo Shi · 2023
Closest in time.