RW-Net: Enhancing Few-Shot Point Cloud Classification with a Wavelet Transform Projection-based Network

TL;DR

RW-Net introduces a wavelet transform and rate-distortion explanation to improve few-shot 3D object classification, achieving state-of-the-art results by focusing on salient features and enhancing generalization from limited data.

Contribution

The paper presents RW-Net, a novel framework combining wavelet transform and RDE to enhance feature extraction and robustness in few-shot 3D classification tasks.

Findings

Achieves state-of-the-art performance on ModelNet40, ModelNet40-C, and ScanObjectNN datasets.

Demonstrates improved generalization and robustness in few-shot learning scenarios.

Effectively captures geometric features using wavelet transform to reduce overfitting.

Abstract

In the domain of 3D object classification, a fundamental challenge lies in addressing the scarcity of labeled data, which limits the applicability of traditional data-intensive learning paradigms. This challenge is particularly pronounced in few-shot learning scenarios, where the objective is to achieve robust generalization from minimal annotated samples. To overcome these limitations, it is crucial to identify and leverage the most salient and discriminative features of 3D objects, thereby enhancing learning efficiency and reducing dependency on large-scale labeled datasets. This work introduces RW-Net, a novel framework designed to address the challenges above by integrating Rate-Distortion Explanation (RDE) and wavelet transform into a state-of-the-art projection-based 3D object classification architecture. The proposed method capitalizes on RDE to extract critical features by identifying and preserving the most informative data components while reducing redundancy. This process ensures the retention of essential information for effective decision-making, optimizing the model's ability to learn from limited data. Complementing RDE, incorporating the wavelet transform further enhances the framework's capability to generalize in low-data regimes. By emphasizing low-frequency components of the input data, the wavelet transform captures fundamental geometric and structural attributes of 3D objects. These attributes are instrumental in mitigating overfitting and improving the robustness of the learned representations across diverse tasks and domains. To validate the effectiveness of our RW-Net, we conduct extensive experiments on three datasets: ModelNet40, ModelNet40-C, and ScanObjectNN for few-shot 3D object classification. The results demonstrate that our approach achieves state-of-the-art performance and exhibits superior generalization and robustness in few-shot learning scenarios.