KAN or MLP? Point Cloud Shows the Way Forward

2025-04-18

AI Summary

To address the limitations of MLP-based point cloud analysis—including weak local geometric modeling due to fixed activation functions, low parameter efficiency, and high model redundancy—this paper proposes PointKAN, the first framework integrating Kolmogorov–Arnold Networks (KANs) into point cloud understanding. Our method introduces three key innovations: (1) a Geometric Affine Module (GAM) to enhance robustness against rigid transformations; (2) a Local–Global Parallel feature processing architecture (LFP/GFP) for hierarchical geometric representation; and (3) an efficient KAN variant—Efficient-KAN—featuring learnable piecewise spline activations and parametric sparse connections. Experiments demonstrate that PointKAN consistently outperforms PointMLP on ModelNet40, ScanObjectNN, and ShapeNetPart. Moreover, it achieves significant gains in few-shot learning while reducing both parameter count and FLOPs substantially.

Technology Category

• Machine Learning: Learning with Manifolds
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Multi-Layer Perceptrons (MLPs) have become one of the fundamental architectural component in point cloud analysis due to its effective feature learning mechanism. However, when processing complex geometric structures in point clouds, MLPs' fixed activation functions struggle to efficiently capture local geometric features, while suffering from poor parameter efficiency and high model redundancy. In this paper, we propose PointKAN, which applies Kolmogorov-Arnold Networks (KANs) to point cloud analysis tasks to investigate their efficacy in hierarchical feature representation. First, we introduce a Geometric Affine Module (GAM) to transform local features, improving the model's robustness to geometric variations. Next, in the Local Feature Processing (LFP), a parallel structure extracts both group-level features and global context, providing a rich representation of both fine details and overall structure. Finally, these features are combined and processed in the Global Feature Processing (GFP). By repeating these operations, the receptive field gradually expands, enabling the model to capture complete geometric information of the point cloud. To overcome the high parameter counts and computational inefficiency of standard KANs, we develop Efficient-KANs in the PointKAN-elite variant, which significantly reduces parameters while maintaining accuracy. Experimental results demonstrate that PointKAN outperforms PointMLP on benchmark datasets such as ModelNet40, ScanObjectNN, and ShapeNetPart, with particularly strong performance in Few-shot Learning task. Additionally, PointKAN achieves substantial reductions in parameter counts and computational complexity (FLOPs). This work highlights the potential of KANs-based architectures in 3D vision and opens new avenues for research in point cloud understanding.

Problem

• Improving local geometric feature capture in point clouds
• Reducing model redundancy and parameter inefficiency
• Enhancing hierarchical feature representation in 3D vision

Innovation

• Uses KANs for point cloud feature representation
• Introduces Geometric Affine Module for robustness
• Develops Efficient-KANs to reduce parameters