Learning Representations from 3D Gaussian Splats

• URL: http://arxiv.org/abs/2605.29549v1
• Date: Thu, 28 May 2026 08:02:17 GMT
• Title: Learning Representations from 3D Gaussian Splats
• Abstract summary: 3D Gaussian Splatting (3DGS) is a recent approach for scene rendering.<n>Although primarily designed for view synthesis, its potential for scene understanding tasks remains underexplored.<n>We benchmark point-based and graph-based models across both traditional point cloud datasets and dedicated Gaussian Splatting datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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