Fetching the paper…
Reading the bibliography…
3D instance segmentation is fundamental to geometric understanding of the world around us.
The partition problem
Sunil Chopra and M. R. Rao · 1993
Earlier work this paper cites.
An optimal graph theoretic approach to data clustering: theory and its application to image segmentation
Z. Wu and R. Leahy · 1993
Earlier work this paper cites.
A density-based algorithm for discovering clusters in large spatial databases with noise
Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu · 1996
Earlier work this paper cites.
Normalized cuts and image segmentation
Jianbo Shi and Jitendra Malik · 2000
Earlier work this paper cites.
Efficient graph-based image segmentation
Pedro F Felzenszwalb and Daniel P Huttenlocher · 2004
Earlier work this paper cites.
Poisson surface reconstruction
Michael Kazhdan, Matthew Bolitho, and Hugues Hoppe · 2006
Earlier work this paper cites.
Geometry of cuts and metrics
Michel Deza and Monique Laurent · 2009
Earlier work this paper cites.
Acquiring 3d indoor environments with variability and repetition
Young Min Kim, Niloy J Mitra, Dong-Ming Yan, and Leonidas Guibas · 2012
Earlier work this paper cites.
A search-classify approach for cluttered indoor scene understanding
Liangliang Nan, Ke Xie, and Andrei Sharf · 2012
Earlier work this paper cites.
Object discovery in 3d scenes via shape analysis
Andrej Karpathy, Stephen Miller, and Li Fei-Fei · 2013
Earlier work this paper cites.
Screened poisson surface reconstruction
Michael Kazhdan and Hugues Hoppe · 2013
Earlier work this paper cites.
Automatic semantic modeling of indoor scenes from low-quality rgb-d data using contextual information
Kang Chen, Yu-Kun Lai, Yu-Xin Wu, Ralph Martin, and Shi-Min Hu · 2014
Earlier work this paper cites.
Database-assisted object retrieval for real-time 3d reconstruction
Yangyan Li, Angela Dai, Leonidas Guibas, and Matthias Nießner · 2015
Earlier work this paper cites.
3d semantic parsing of large-scale indoor spaces
Iro Armeni, Ozan Sener, Amir R. Zamir, Helen Jiang, Ioannis Brilakis, Martin Fischer, and Silvio Savarese · 2016
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.
Accelerated hierarchical density based clustering
Leland McInnes and John Healy · 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.
Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations
Carole H Sudre, Wenqi Li, Tom Vercauteren, Sebastien Ourselin, and M Jorge Cardoso · 2017
Earlier work this paper cites.
3dmv: Joint 3d-multi-view prediction for 3d semantic scene segmentation
Angela Dai and Matthias Nießner · 2018
Cited alongside, same era.
3d semantic segmentation with submanifold sparse convolutional networks
Benjamin Graham, Martin Engelcke, and Laurens van der Maaten · 2018
Cited alongside, same era.
Fully-convolutional point networks for large-scale point clouds
Dario Rethage, Johanna Wald, Jurgen Sturm, Nassir Navab, and Federico Tombari · 2018
Cited alongside, same era.
Sgpn: Similarity group proposal network for 3d point cloud instance segmentation
Weiyue Wang, Ronald Yu, Qiangui Huang, and Ulrich Neumann · 2018
Cited alongside, same era.
4d spatio-temporal convnets: Minkowski convolutional neural networks
Christopher Choy, JunYoung Gwak, and Silvio Savarese · 2019
Cited alongside, same era.
3d-sis: 3d semantic instance segmentation of rgb-d scans
Ji Hou, Angela Dai, and Matthias Nießner · 2019
Instance segmentation in 3d scenes using semantic superpoint tree networks
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, and Kui Jia · 2021
Later among the works it cites.
One thing one click: A self-training approach for weakly supervised 3d semantic segmentation
Zhengzhe Liu, Xiaojuan Qi, and Chi-Wing Fu · 2021
Later among the works it cites.
Self-supervised pretraining of 3d features on any point-cloud
Zaiwei Zhang, Rohit Girdhar, Armand Joulin, and Ishan Misra · 2021
Later among the works it cites.
Box2mask: Weakly supervised 3d semantic instance segmentation using bounding boxes
Julian Chibane, Francis Engelmann, Tuan Anh Tran, and Gerard Pons-Moll · 2022
Later among the works it cites.
Learning superpoint graph cut for 3d instance segmentation
Le Hui, Linghua Tang, Yaqi Shen, Jin Xie, and Jian Yang · 2022
Later among the works it cites.
Partslip: Low-shot part segmentation for 3d point clouds via pretrained image-language models
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Multi-view pointnet for 3d scene understanding
Maximilian Jaritz, Jiayuan Gu, and Hao Su · 2019
Cited alongside, same era.
Incremental class discovery for semantic segmentation with rgbd sensing
Yoshikatsu Nakajima, Byeongkeun Kang, Hideo Saito, and Kris Kitani · 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.
Pointconv: Deep convolutional networks on 3d point clouds
Wenxuan Wu, Zhongang Qi, and Li Fuxin · 2019
Cited alongside, same era.
Occuseg: Occupancy-aware 3d instance segmentation
Lei Han, Tian Zheng, Lan Xu, and Lu Fang · 2020
Cited alongside, same era.
Randla-net: Efficient semantic segmentation of large-scale point clouds
Qingyong Hu, Bo Yang, Linhai Xie, Stefano Rosa, Yulan Guo, Zhihua Wang, Niki Trigoni, and Andrew Markham · 2020
Cited alongside, same era.
Minghua Liu, Yinhao Zhu, H. Cai, Shizhong Han, Z. Ling, Fatih Murat Porikli, and Hao Su · 2022
Later among the works it cites.
Language-grounded indoor 3d semantic segmentation in the wild
David Rozenberszki, Or Litany, and Angela Dai · 2022
Later among the works it cites.
Clip-fields: Weakly supervised semantic fields for robotic memory
Nur Muhammad Mahi Shafiullah, Chris Paxton, Lerrel Pinto, Soumith Chintala, and Arthur Szlam · 2022
Later among the works it cites.
OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Clouds
Ziyang Song and Bo Yang · 2022
Later among the works it cites.
Freesolo: Learning to segment objects without annotations
Xinlong Wang, Zhiding Yu, Shalini De Mello, Jan Kautz, Anima Anandkumar, Chunhua Shen, and Jose M Alvarez · 2022
Later among the works it cites.
Pla: Language-driven open-vocabulary 3d scene understanding
Runyu Ding, Jihan Yang, Chuhui Xue, Wenqing Zhang, Song Bai, and Xiaojuan Qi · 2023
Closest in time.
Mask3d: Pre-training 2d vision transformers by learning masked 3d priors
Ji Hou, Xiaoliang Dai, Zijian He, Angela Dai, and Matthias Nießner · 2023
Closest in time.
Conceptfusion: Open-set multimodal 3d mapping
Krishna Murthy Jatavallabhula, Alihusein Kuwajerwala, Qiao Gu, Mohd Omama, Tao Chen, Shuang Li, Ganesh Iyer, Soroush Saryazdi, Nikhil Keetha, Ayush Tewari, Joshua B. Tenenbaum, Celso Miguel de Melo, Madhava Krishna, Liam Paull, Florian Shkurti, and Antonio Torralba · 2023
Closest in time.
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al · 2023
Closest in time.
Top-down beats bottom-up in 3d instance segmentation, 2023
Maksim Kolodiazhnyi, Danila Rukhovich, Anna Vorontsova, and Anton Konushin · 2023
Closest in time.
Mask3D for 3D Semantic Instance Segmentation
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, and Bastian Leibe · 2023
Closest in time.
Superpoint transformer for 3d scene instance segmentation
Jiahao Sun, Chunmei Qing, Junpeng Tan, and Xiangmin Xu · 2023
Closest in time.
Sam3d: Segment anything in 3d scenes
Yunhan Yang, Xiaoyang Wu, Tong He, Hengshuang Zhao, and Xihui Liu · 2023
Closest in time.