Name
Title | ||
|---|---|---|
PolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape Representation |
Abstract | ||
|---|---|---|
3D shape representation and its processing have substantial effects on 3D shape recognition. The polygon mesh as a 3D shape representation has many advantages in computer graphics and geometry processing. However, there are still some challenges for the existing deep neural network (DNN)-based methods on polygon mesh representation, such as handling the variations in the degree and permutations of the vertices and their pairwise distances. To overcome these challenges, we propose a DNN-based method (PolyNet) and a specific polygon mesh representation (PolyShape) with a multi-resolution structure. PolyNet contains two operations; (1) a polynomial convolution (PolyConv) operation with learnable coefficients, which learns continuous distributions as the convolutional filters to share the weights across different vertices, and (2) a polygonal pooling (PolyPool) procedure by utilizing the multi-resolution structure of PolyShape to aggregate the features in a much lower dimension. Our experiments demonstrate the strength and the advantages of PolyNet on both 3D shape classification and retrieval tasks compared to existing polygon mesh-based methods and its superiority in classifying graph representations of images. The code is publicly available from this link. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/3DV53792.2021.00109 | 2021 INTERNATIONAL CONFERENCE ON 3D VISION (3DV 2021) |
DocType | ISSN | Citations |
Conference | 2378-3826 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohsen Yavartanoo | 1 | 0 | 0.34 |
| Shih-Hsuan Hung | 2 | 0 | 1.35 |
| Reyhaneh Neshatavar | 3 | 0 | 0.34 |
| Yue Zhang | 4 | 23 | 8.01 |
| Kyoung Mu Lee | 5 | 3228 | 153.84 |