Zhang, Y;
Lu, Z;
Xue, J-H;
Liao, Q;
(2019)
A New Rotation-Invariant Deep Network for 3D Object Recognition.
In: Karam, Lina J and Mei, Tao and Wu, Feng, (eds.)
Proceedings of the 2019 IEEE International Conference on Multimedia and Expo (ICME).
IEEE
Preview |
Text
YachiZhang-ICME2019.pdf - Accepted Version Download (616kB) | Preview |
Abstract
When inputs are rotated, most 3D convolutional neural networks (CNNs) will have their performance much dropped, especially for those models with voxelized input of 3D objects. The newly proposed Spherical CNNS, with the concept of the rotation-equivariant spherical correlation, aims to achieve rotation invariance. Inspired by this, we propose a new rotation-invariant deep network to recognize rotated 3D objects. Specifically, we adopt the spherical representation and the spherical correlation S^2 layer of Spherical CNNs, for their capacity of representing 3D objects and rotation equivariance. In the meantime, we improve the computational efficiency and expressiveness of Spherical CNNs, by replacing its time-consuming and depth-limited SO(3) layer with a PointNet-style network architecture. Hence our proposed network can maintain the equivariance as the network grows deeper while substantially reducing its runtime, leading to a much better efficiency and expressiveness of rotation-invariant representation. Experimental results show that our network performs better than or comparable to the state-of-the-art methods in the ModelNet40 classification challenge.
| Type: | Proceedings paper |
|---|---|
| Title: | A New Rotation-Invariant Deep Network for 3D Object Recognition |
| ISBN: | 978-1-5386-9552-4 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/icme.2019.00277 |
| Publisher version: | https://doi.org/10.1109/ICME.2019.00277 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Three-dimensional displays, Correlation, Two-dimensional displays, Feature extraction, Solid modeling, Convolution, Training, Deep learning, Object recognition, Rotation invariant, 3D representation |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10079666 |
Archive Staff Only
 |
View Item |
