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This paper studies the 3D instance segmentation problem, which has a variety of real-world applications such as robotics and augmented reality.
“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.
“Feature pyramid networks for object detection,”
Tsung-Yi Lin, Piotr Dollár, Ross B. Girshick, Kaiming He, Bharath Hariharan, and Serge J. Belongie, · 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.
“3d-rcnn: Instance-level 3d object reconstruction via render-and-compare,”
Abhijit Kundu, Yin Li, and James M. Rehg, · 2018
Earlier work this paper cites.
“SGPN: similarity group proposal network for 3d point cloud instance segmentation,”
Weiyue Wang, Ronald Yu, Qiangui Huang, and Ulrich Neumann, · 2018
Earlier work this paper cites.
“3d semantic segmentation with submanifold sparse convolutional networks,”
Benjamin Graham, Martin Engelcke, and Laurens van der Maaten, · 2018
Earlier work this paper cites.
“Acquisition of localization confidence for accurate object detection,”
Borui Jiang, Ruixuan Luo, Jiayuan Mao, Tete Xiao, and Yuning Jiang, · 2018
Earlier work this paper cites.
“Learning object bounding boxes for 3d instance segmentation on point clouds,”
Bo Yang, Jianan Wang, Ronald Clark, Qingyong Hu, Sen Wang, Andrew Markham, and Niki Trigoni, · 2019
Earlier work this paper cites.
“3d-sis: 3d semantic instance segmentation of rgb-d scans,”
Ji Hou, Angela Dai, and Matthias Nießner, · 2019
Earlier work this paper cites.
“JSIS3D: joint semantic-instance segmentation of 3d point clouds with multi-task pointwise networks and multi-value conditional random fields,”
Quang-Hieu Pham, Duc Thanh Nguyen, Binh-Son Hua, Gemma Roig, and Sai-Kit Yeung, · 2019
Earlier work this paper cites.
“Associatively segmenting instances and semantics in point clouds,”
Xinlong Wang, Shu Liu, Xiaoyong Shen, Chunhua Shen, and Jiaya Jia, · 2019
Earlier work this paper cites.
“Deep hough voting for 3d object detection in point clouds,”
Charles R. Qi, Or Litany, Kaiming He, and Leonidas J. Guibas, · 2019
Cited alongside, same era.
“Hierarchy denoising recursive autoencoders for 3d scene layout prediction,”
Yifei Shi, Angel X Chang, Zhelun Wu, Manolis Savva, and Kai Xu, · 2019
Cited alongside, same era.
“3d scene graph: A structure for unified semantics, 3d space, and camera,”
Iro Armeni, Zhi-Yang He, JunYoung Gwak, Amir R Zamir, Martin Fischer, Jitendra Malik, and Silvio Savarese, · 2019
Cited alongside, same era.
“3d instance segmentation via multi-task metric learning,”
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, and Martin R Oswald, · 2019
Cited alongside, same era.
“3d scene graph: A structure for unified semantics, 3d space, and camera,”
Iro Armeni, Zhi-Yang He, Amir Roshan Zamir, JunYoung Gwak, Jitendra Malik, Martin Fischer, and Silvio Savarese, · 2019
Cited alongside, same era.
“3d instance embedding learning with a structure-aware loss function for point cloud segmentation,”
Zhidong Liang, Ming Yang, Hao Li, and Chunxiang Wang, · 2020
Later among the works it cites.
“Learning gaussian instance segmentation in point clouds,”
Shih-Hung Liu, Shang-Yi Yu, Shao-Chi Wu, Hwann-Tzong Chen, and Tyng-Luh Liu, · 2020
Later among the works it cites.
“Learning and memorizing representative prototypes for 3d point cloud semantic and instance segmentation,”
Tong He, Dong Gong, Zhi Tian, and Chunhua Shen, · 2020
Later among the works it cites.
“Instance-aware embedding for point cloud instance segmentation,”
Tong He, Yifan Liu, Chunhua Shen, Xinlong Wang, and Changming Sun, · 2020
Later among the works it cites.
“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.
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Yifei Shi, Angel X. Chang, Zhelun Wu, Manolis Savva, and Kai Xu, · 2019
Cited alongside, same era.
“Gs3d: An efficient 3d object detection framework for autonomous driving,”
Buyu Li, Wanli Ouyang, Lu Sheng, Xingyu Zeng, and Xiaogang Wang, · 2019
Cited alongside, same era.
“Panopticfusion: Online volumetric semantic mapping at the level of stuff and things,”
Gaku Narita, Takashi Seno, Tomoya Ishikawa, and Yohsuke Kaji, · 2019
Cited alongside, same era.
“3d-mpa: Multi-proposal aggregation for 3d semantic instance segmentation,”
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, and Matthias Nießner, · 2020
Cited alongside, same era.
“Pointgroup: Dual-set point grouping for 3d instance segmentation,”
Li Jiang, Hengshuang Zhao, Shaoshuai Shi, Shu Liu, Chi-Wing Fu, and Jiaya Jia, · 2020
Cited alongside, same era.
“Occuseg: Occupancy-aware 3d instance segmentation,”
Lei Han, Tian Zheng, Lan Xu, and Lu Fang, · 2020
Cited alongside, same era.
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, and Xinggang Wang, · 2021
Later among the works it cites.
“An empirical study of adder neural networks for object detection,”
Xinghao Chen, Chang Xu, Minjing Dong, Chunjing Xu, and Yunhe Wang, · 2021
Later among the works it cites.
“DyCo3d: Robust instance segmentation of 3d point clouds through dynamic convolution,”
Tong He, Chunhua Shen, and Anton van den Hengel, · 2021
Later among the works it cites.
“Point cloud instance segmentation using probabilistic embeddings,”
Biao Zhang and Peter Wonka, · 2021
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“Icm-3d: Instantiated category modeling for 3d instance segmentation,”
Ruihang Chu, Yukang Chen, Tao Kong, Lu Qi, and Lei Li, · 2021
Later among the works it cites.