用高斯点云提升3D分类中模糊物体的识别准确率
Mitigating Ambiguities in 3D Classification with Gaussian Splatting
- 利用高斯点云的尺度与旋转特征区分线状与平面表面
- 通过透明度信息有效解决透明/反光物体的分类歧义
- 适用于需要高精度3D分类的工业检测与自动驾驶场景
基于点云的3D分类是3D视觉中的基础问题。由于点云表示离散且材质描述不足，导致在区分线状与平面表面、透明或反射物体时存在歧义。为此，本文提出基于高斯点云（Gaussian Splatting, GS）的3D分类方法。研究发现，高斯点云中的尺度与旋转系数可表征表面类型：线状表面由多个细长高斯椭球组成，而平面表面则由少数扁平高斯椭球构成；同时，高斯点云中的不透明度反映了物体的透明特性。由此，可有效缓解基于点云的3D分类歧义。为验证有效性，我们构建了首个真实世界高斯点云数据集，包含20个类别，每类200个物体。实验表明，采用高斯点云输入在区分模糊物体方面表现更优，并具备跨不同分类模型的泛化能力。
原文摘要 · Abstract (English)
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.
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