Non-rigid 3D Shape Retrieval

By Z. Lian, J. Zhang, S. Choi, H. ElNaghy, Jihad El-Sana, Takahiko Furuya, Andrea Giachetti, Riza Alp Güler, L. Lai, C. Li and 16 more. 2015

68 citations recorded by the source corpus.

How researchers see this paper

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

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space (2017, methodology): “Shapes in SHREC15 are 2D surfaces embedded in 3D space.”
Zero-shot Learning of 3D Point Cloud Objects (2019, methodology): “Dataset: We perform our experiments on three well-known 3D datasets, ModelNet40/Modelnet10 [29], McGill [21], and SHREC2015 [12].”
Mitigating the Hubness Problem for Zero-Shot Learning of 3D Objects (2019, methodology): “1 Setup Dataset: In this paper, four 3D datasets, ModelNet40 [39], ModelNet10 [39], McGill [34], and SHREC2015 [21], are used to evaluate our proposed method.”
PU-Net: Point Cloud Upsampling Network (2018, methodology): “To further demonstrate the generalization ability of our network, we directly test our well-trained network on the SHREC15 [22] dataset, which contains 1200 shapes from 50 categories.”
Transductive Zero-Shot Learning for 3D Point Cloud Classification (2019, methodology): “Our main contributions are: (1) extending and adapting transductive zero-shot learning and generalized zeroshot learning to 3D point cloud classification for the first time; (2) developing a novel triplet loss that takes advantage of unlabeled test data, applicable to both 3D point cloud data and 2D”