Mitigating Ambiguities in 3D Classification with Gaussian Splatting

2025-03-11

AI Summary

To address the inherent ambiguity in 3D point cloud classification—particularly in distinguishing linear/surface-like structures and transparent/reflective objects—this work pioneers the integration of Gaussian Splatting (GS) into this task, leveraging its explicit scale, rotation, and opacity parameters to model surface geometry and material physical properties. Our contributions are threefold: (1) a novel GS-based point cloud classification framework incorporating scale-rotation covariance encoding and an opacity-driven transparency-aware module; (2) the first real-world GS point cloud dataset, comprising 20 classes × 200 samples; and (3) an interpretable mapping between GS parameters and underlying physical surface attributes. Experiments demonstrate significant accuracy gains on ambiguous samples, strong cross-architecture generalization (e.g., PointNet++, PointMLP), and consistent superiority over state-of-the-art baselines using raw point clouds.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Machine Learning: Transparent, Interpretable, Explainable ML
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

3D classification with point cloud input is a fundamental problem in 3D vision. However, due to the discrete nature and the insufficient material description of point cloud representations, there are ambiguities in distinguishing wire-like and flat surfaces, as well as transparent or reflective objects. To address these issues, we propose Gaussian Splatting (GS) point cloud-based 3D classification. We find that the scale and rotation coefficients in the GS point cloud help characterize surface types. Specifically, wire-like surfaces consist of multiple slender Gaussian ellipsoids, while flat surfaces are composed of a few flat Gaussian ellipsoids. Additionally, the opacity in the GS point cloud represents the transparency characteristics of objects. As a result, ambiguities in point cloud-based 3D classification can be mitigated utilizing GS point cloud as input. To verify the effectiveness of GS point cloud input, we construct the first real-world GS point cloud dataset in the community, which includes 20 categories with 200 objects in each category. Experiments not only validate the superiority of GS point cloud input, especially in distinguishing ambiguous objects, but also demonstrate the generalization ability across different classification methods.

Problem

• Ambiguities in distinguishing wire-like and flat surfaces in 3D classification
• Challenges in identifying transparent or reflective objects in point cloud data
• Insufficient material description in traditional point cloud representations

Innovation

• Gaussian Splatting for 3D classification
• Scale and rotation coefficients characterize surfaces
• Opacity represents object transparency in GS