3D Shape Segmentation and Labeling via Extreme Learning Machine
By Zhige Xie, Kai Xu, Ligang Liu, Yueshan Xiong. 2014
93 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Antonio Torralba
- Chuang Gan
- Josh Tenenbaum
- Andrea Vedaldi
- Thomas Brox
- Leonidas J. Guibas
- Yining Hong
- Mohit Bansal
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Leonidas J. Guibas
- Jitendra Malik
- Andrea Vedaldi
- Ming-Hsuan Yang
- Dacheng Tao
- Alan L. Yuille
- Trevor Darrell
In Industrial and Manufacturing Engineering
- Leonidas J. Guibas
- Hao Su
- Alla Sheffer
- Li Yi
- Vladimir G. Kim
- Thomas Brox
- Qi-Xing Huang
- Antonio Torralba
Citations in the corpus
- SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation (2016, methodology): “Along this track, most previous methods [12, 28, 18, 7] employ traditional machine learning techniques and construct classifiers based on geometric features.”
- 3D shape segmentation via shape fully convolutional networks (2017, methodology): “used to complete the related training [17, 18, 19, 20, 33].”
- Efficient Decomposition of Image and Mesh Graphs by Lifted Multicuts (2015, methodology): “We compute informative features known to provide good results in previous work [24, 14, 39]: curvatures (minimum, maximum, Gaussian and mean) computed at two different scales, shape diameter [36] and dihedral angle.”
- PTR: A Benchmark for Part-based Conceptual, Relational, and Physical Reasoning (2021, background): “Restricted by the annotations in existing large-scale datasets, previous part-based 3D understanding works mainly focus on identifying parts [15, 30, 26, 55, 32, 58] and part relations [6, 10, 31, 35, 60, 50] for individual 3D objects.”
- 3D Mesh Labeling via Deep Convolutional Neural Networks (2015, methodology): “On the contrary, supervised or semisupervised approaches [ Kalogerakis et al. 2010; Lv et al. 2012; Wang et al. 2012, 2013; Xie et al. 2014] were also proposed to tackle this problem.”
- A scalable active framework for region annotation in 3D shape collections (2016, methodology): “Supervised learning is frequently used to obtain semantic annotations of 3D shapes [Kalogerakis et al. 2010; Lv et al. 2012; Xie et al. 2014; Makadia and Yumer 2014; Guo et al. 2015].”