探索3D高斯点云的场景理解潜力，验证不同模型在该表示下的表现。
Learning Representations from 3D Gaussian Splats
- 用点、图结构模型对比评估高斯点云的表征能力。
- 在点云与高斯点云数据集上均验证了模型分类性能差异。
- 揭示高斯特有属性对表征质量的关键影响，适合几何学习研究者。
3D高斯点阵（3DGS）是一种新兴的场景渲染方法，虽主要面向视图合成，其在场景理解任务中的潜力尚未充分探索。本文对多种几何深度学习架构在基于高斯点阵表示的3D场景分类中进行了对比评估。我们在传统点云数据集和专用高斯点阵数据集上，测试了基于点和基于图的模型。通过端到端分类、线性探测和聚类分析，对场景嵌入的潜在表征进行评估。研究揭示了不同架构家族之间的稳定差异，并说明高斯特定属性对表征质量的重要影响，为选择合适架构和特征配置提供了依据。
原文摘要 · Abstract (English)
3D Gaussian Splatting (3DGS) is a recent approach for scene rendering. Although primarily designed for view synthesis, its potential for scene understanding tasks remains underexplored. In this work, we conduct a comparative evaluation of various geometric deep learning architectures for the classification of 3D scenes represented using Gaussian Splatting. We benchmark point-based and graph-based models across both traditional point cloud datasets and dedicated Gaussian Splatting datasets. Scenes are embedded into latent representations, which are evaluated through end-to-end classification, linear probing, and clustering analysis. Our study provides insight into the suitability of different geometry-aware architectures and input feature configurations for learning effective 3D Gaussian Splat representations. The results highlight consistent differences between architectural families and reveal the impact of Gaussian-specific attributes on the quality of representation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。