Fetching the paper…
Reading the bibliography…
Given a 3D object, kinematic motion prediction aims to identify the mobile parts as well as the corresponding motion parameters.
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
Martin A Fischler and Robert C Bolles · 1981
Earlier work this paper cites.
Signature verification using a” siamese” time delay neural network
Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard Säckinger, and Roopak Shah · 1993
Earlier work this paper cites.
Automatic kinematic chain building from feature trajectories of articulated objects
Jingyu Yan and Marc Pollefeys · 2006
Earlier work this paper cites.
The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
Earlier work this paper cites.
Joint-aware manipulation of deformable models
Weiwei Xu, Jun Wang, KangKang Yin, Kun Zhou, Michiel Van De Panne, Falai Chen, and Baining Guo · 2009
Earlier work this paper cites.
Illustrating how mechanical assemblies work
Niloy J Mitra, Yong-Liang Yang, Dong-Ming Yan, Wilmot Li, Maneesh Agrawala, et al · 2010
Earlier work this paper cites.
Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee et al · 2013
Earlier work this paper cites.
Meta-representation of shape families
Noa Fish, Melinos Averkiou, Oliver Van Kaick, Olga Sorkine-Hornung, Daniel Cohen-Or, and Niloy J Mitra · 2014
Earlier work this paper cites.
Interaction context (icon) towards a geometric functionality descriptor
Ruizhen Hu, Chenyang Zhu, Oliver van Kaick, Ligang Liu, Ariel Shamir, and Hao Zhang · 2015
Earlier work this paper cites.
Weakly supervised deep detection networks
Hakan Bilen and Andrea Vedaldi · 2016
Earlier work this paper cites.
Weakly supervised cascaded convolutional networks
Ali Diba, Vivek Sharma, Ali Pazandeh, Hamed Pirsiavash, and Luc Van Gool · 2017
Cited alongside, same era.
Learning to predict part mobility from a single static snapshot
Ruizhen Hu, Wenchao Li, Oliver Van Kaick, Ariel Shamir, Hao Zhang, and Hui Huang · 2017
Cited alongside, same era.
Multiple instance detection network with online instance classifier refinement
Peng Tang, Xinggang Wang, Xiang Bai, and Wenyu Liu · 2017
Cited alongside, same era.
On regularized losses for weakly-supervised cnn segmentation
Meng Tang, Federico Perazzi, Abdelaziz Djelouah, Ismail Ben Ayed, Christopher Schroers, and Yuri Boykov · 2018
Cited alongside, same era.
Unsupervised domain adaptation for semantic segmentation via class-balanced self-training
Yang Zou, Zhiding Yu, BVK Kumar, and Jinsong Wang · 2018
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
Later among the works it cites.
Pseudo-labeling and confirmation bias in deep semi-supervised learning
Eric Arazo, Diego Ortego, Paul Albert, Noel E O’Connor, and Kevin McGuinness · 2020
Later among the works it cites.
Weakly-supervised semantic segmentation via sub-category exploration
Yu-Ting Chang, Qiaosong Wang, Wei-Chih Hung, Robinson Piramuthu, Yi-Hsuan Tsai, and Ming-Hsuan Yang · 2020
Later among the works it cites.
Category-level articulated object pose estimation
Xiaolong Li, He Wang, Li Yi, Leonidas J Guibas, A Lynn Abbott, and Shuran Song · 2020
Later among the works it cites.
The pybullet module-based approach to control the collaborative yumi robot
Roman Michalík and Aleš Janota · 2020
Later among the works it cites.
SAPIEN: A simulated part-based interactive environment
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Label propagation for deep semi-supervised learning
Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, and Ondrej Chum · 2019
Cited alongside, same era.
Structurenet: hierarchical graph networks for 3d shape generation
Kaichun Mo, Paul Guerrero, Li Yi, Hao Su, Peter Wonka, Niloy J Mitra, and Leonidas J Guibas · 2019
Cited alongside, same era.
Partnet: A large-scale benchmark for fine-grained and hierarchical part-level 3d object understanding
Kaichun Mo, Shilin Zhu, Angel X Chang, Li Yi, Subarna Tripathi, Leonidas J Guibas, and Hao Su · 2019
Cited alongside, same era.
Box-driven class-wise region masking and filling rate guided loss for weakly supervised semantic segmentation
Chunfeng Song, Yan Huang, Wanli Ouyang, and Liang Wang · 2019
Cited alongside, same era.
Shape2motion: Joint analysis of motion parts and attributes from 3d shapes
Xiaogang Wang, Bin Zhou, Yahao Shi, Xiaowu Chen, Qinping Zhao, and Kai Xu · 2019
Cited alongside, same era.
Fanbo Xiang, Yuzhe Qin, Kaichun Mo, Yikuan Xia, Hao Zhu, Fangchen Liu, Minghua Liu, Hanxiao Jiang, Yifu Yuan, He Wang, Li Yi, Angel X. Chang, Leonidas J. Guibas, and Hao Su · 2020
Later among the works it cites.
Rpm-net: recurrent prediction of motion and parts from point cloud
Zihao Yan, Ruizhen Hu, Xingguang Yan, Luanmin Chen, Oliver Van Kaick, Hao Zhang, and Hui Huang · 2020
Later among the works it cites.
Unsupervised pose-aware part decomposition for 3d articulated objects
Yuki Kawana, Yusuke Mukuta, and Tatsuya Harada · 2021
Later among the works it cites.
Unsupervised kinematic motion detection for part-segmented 3d shape collections
Xianghao Xu, Yifan Ruan, Srinath Sridhar, and Daniel Ritchie · 2022
Later among the works it cites.
Dsg-net: Learning disentangled structure and geometry for 3d shape generation
Jie Yang, Kaichun Mo, Yu-Kun Lai, Leonidas J Guibas, and Lin Gao · 2022
Later among the works it cites.