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This paper proposes a cross-modal distillation framework, PartDistill, which transfers 2D knowledge from vision-language models (VLMs) to facilitate 3D shape part segmentation.
A flexible new technique for camera calibration
Zhengyou Zhang · 2000
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Model compression
Cristian Buciluǎ, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
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Active co-analysis of a set of shapes
Yunhai Wang, Shmulik Asafi, Oliver Van Kaick, Hao Zhang, Daniel Cohen-Or, and Baoquan Chen · 2012
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Shapenet: An information-rich 3D model repository
Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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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
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3D shape segmentation with projective convolutional networks
Evangelos Kalogerakis, Melinos Averkiou, Subhransu Maji, and Siddhartha Chaudhuri · 2017
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Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas · 2017
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Deep learning using rectified linear units (relu)
Abien Fred Agarap · 2018
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Large-scale point cloud semantic segmentation with superpoint graphs
Loic Landrieu and Martin Simonovsky · 2018
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Spaghetti labeling: Directed acyclic graphs for block-based connected components labeling
Federico Bolelli, Stefano Allegretti, Lorenzo Baraldi, and Costantino Grana · 2019
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Class-balanced loss based on effective number of samples
Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, and Serge Belongie · 2019
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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
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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A survey of zero-shot learning: Settings, methods, and applications
Wei Wang, Vincent W Zheng, Han Yu, and Chunyan Miao · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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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
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Label-efficient learning on point clouds using approximate convex decompositions
Matheus Gadelha, Aruni RoyChowdhury, Gopal Sharma, Evangelos Kalogerakis, Liangliang Cao, Erik Learned-Miller, Rui Wang, and Subhransu Maji · 2020
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Accelerating 3D deep learning with pytorch3d
Justin Johnson, Nikhila Ravi, Jeremy Reizenstein, David Novotny, Shubham Tulsiani, Christoph Lassner, and Steve Branson · 2020
Cited alongside, same era.
Neuform: Adaptive overfitting for neural shape editing
Connor Lin, Niloy Mitra, Gordon Wetzstein, Leonidas J Guibas, and Paul Guerrero · 2022
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Image segmentation using text and image prompts
Timo Lüddecke and Alexander Ecker · 2022
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Text2mesh: Text-driven neural stylization for meshes
Oscar Michel, Roi Bar-On, Richard Liu, Sagie Benaim, and Rana Hanocka · 2022
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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
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Image-to-lidar self-supervised distillation for autonomous driving data
Corentin Sautier, Gilles Puy, Spyros Gidaris, Alexandre Boulch, Andrei Bursuc, and Renaud Marlet · 2022
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Point mixswap: Attentional point cloud mixing via swapping matched structural divisions
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Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies
John D Kelleher, Brian Mac Namee, and Aoife D’arcy · 2020
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Double similarity distillation for semantic image segmentation
Yingchao Feng, Xian Sun, Wenhui Diao, Jihao Li, and Xin Gao · 2021
Cited alongside, same era.
Learning 3D semantic segmentation with only 2D image supervision
Kyle Genova, Xiaoqi Yin, Abhijit Kundu, Caroline Pantofaru, Forrester Cole, Avneesh Sud, Brian Brewington, Brian Shucker, and Thomas Funkhouser · 2021
Cited alongside, same era.
Scaling up visual and vision-language representation learning with noisy text supervision
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig · 2021
Cited alongside, same era.
Shape as points: A differentiable poisson solver
Songyou Peng, Chiyu Jiang, Yiyi Liao, Michael Niemeyer, Marc Pollefeys, and Andreas Geiger · 2021
Cited alongside, same era.
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
Cited alongside, same era.
A revisit of shape editing techniques: From the geometric to the neural viewpoint
Yu-Jie Yuan, Yu-Kun Lai, Tong Wu, Lin Gao, and Ligang Liu · 2021
Cited alongside, same era.
Ardian Umam, Cheng-Kun Yang, Yung-Yu Chuang, Jen-Hui Chuang, and Yen-Yu Lin · 2022
Later among the works it cites.
SATR: Zero-shot semantic segmentation of 3D shapes
Ahmed Abdelreheem, Ivan Skorokhodov, Maks Ovsjanikov, and Peter Wonka · 2023
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Clip2scene: Towards label-efficient 3D scene understanding by clip
Runnan Chen, Youquan Liu, Lingdong Kong, Xinge Zhu, Yuexin Ma, Yikang Li, Yuenan Hou, Yu Qiao, and Wenping Wang · 2023
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Self-supervised image-to-point distillation via semantically tolerant contrastive loss
Anas Mahmoud, Jordan SK Hu, Tianshu Kuai, Ali Harakeh, Liam Paull, and Steven L Waslander · 2023
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DiT-3D: Exploring plain diffusion transformers for 3D shape generation
Shentong Mo, Enze Xie, Ruihang Chu, Lewei Yao, Lanqing Hong, Matthias Nießner, and Zhenguo Li · 2023
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Openscene: 3D scene understanding with open vocabularies
Songyou Peng, Kyle Genova, Chiyu Jiang, Andrea Tagliasacchi, Marc Pollefeys, Thomas Funkhouser, et al · 2023
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Demystifying deep learning: An introduction to the mathematics of neural networks
Douglas J Santry · 2023
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2D-3D interlaced transformer for point cloud segmentation with scene-level supervision
Cheng-Kun Yang, Min-Hung Chen, Yung-Yu Chuang, and Yen-Yu Lin · 2023
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Pointclip v2: Adapting clip for powerful 3D open-world learning
Xiangyang Zhu, Renrui Zhang, Bowei He, Ziyao Zeng, Shanghang Zhang, and Peng Gao · 2023
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Segment everything everywhere all at once
Xueyan Zou, Jianwei Yang, Hao Zhang, Feng Li, Linjie Li, Jianfeng Gao, and Yong Jae Lee · 2023
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