Learning 3D Part Detection from Sparsely Labeled Data

By Ameesh Makadia, Mehmet Ersin Yümer. 2014

12 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Geology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Leonidas J. Guibas
2. Hao Su
3. Li Yi
4. Charles Ruizhongtai Qi
5. Vladimir G. Kim
6. Matthias Nießner
7. Minhyuk Sung
8. Angela Dai

In Computer Vision and Pattern Recognition

1. Leonidas J. Guibas
2. Luc Van Gool
3. Hao Su
4. Jitendra Malik
5. Li Yi
6. Trevor Darrell
7. Andrea Vedaldi
8. Xiaogang Wang

In Geology

1. Leonidas J. Guibas
2. Li Yi
3. Hao Su
4. Charles Ruizhongtai Qi
5. Minhyuk Sung
6. Vladimir G. Kim
7. Matthias Nießner
8. Roland Angst

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.”
• Data-driven structural priors for shape completion (2015, background): “…developed for detecting part structure of unlabeled shapes without any supervision [Golovinskiy and Funkhouser 2009; Sidi et al. 2011; Hu et al. 2012; Huang et al. 2011; Huang et al. 2014] or with some user input [Kalogerakis et al. 2010; Xie et al. 2014; Makadia and Yumer 2014; Yumer et al. 2014].”