A naukri.com initiative
Arxiv
1d
154
Image Credit: Arxiv
PointNet with KAN versus PointNet with MLP for 3D Classification and Segmentation of Point Sets
- Kolmogorov-Arnold Networks (KANs) are being explored as an alternative to Multilayer Perceptrons (MLPs) in deep learning.
- KANs have been integrated into various deep learning architectures, but their use in point-cloud-based neural networks has not been studied.
- A new model, PointNet-KAN, combines KANs instead of MLPs in the PointNet framework for 3D point cloud classification and segmentation.
- PointNet-KAN utilizes shared KAN layers and symmetric functions to maintain permutation invariance for global feature extraction.
- Unlike MLPs that train weights and biases with fixed activation functions, KANs aim to train the activation functions themselves.
- Jacobi polynomials are used to construct the KAN layers in this new model.
- Extensive evaluations show that PointNet-KAN performs competitively with PointNet using MLPs on benchmark datasets for 3D object classification and segmentation.
- Despite being a simpler architecture, PointNet-KAN achieves good results, even with shallower networks.
- A hybrid model incorporating both KAN and MLP layers was also studied in this work.
- The study aims to lay a foundation for integrating KANs into more advanced point cloud processing architectures.
Read Full Article
9 Likes
For uninterrupted reading, download the app