TL;DR
This paper introduces 3DGS-to-PC, a flexible framework that converts 3D Gaussian Splatting scenes into dense, accurate point clouds and meshes, facilitating broader compatibility with standard 3D processing tools.
Contribution
The work presents a novel, customizable method for converting 3D Gaussian Splatting scenes into point clouds and meshes without retraining, using probabilistic sampling and color recalibration.
Findings
Produces high-accuracy point clouds closely matching original scenes
Supports mesh generation via Poisson Surface Reconstruction
Integrates seamlessly into existing 3DGS pipelines
Abstract
3D Gaussian Splatting (3DGS) excels at producing highly detailed 3D reconstructions, but these scenes often require specialised renderers for effective visualisation. In contrast, point clouds are a widely used 3D representation and are compatible with most popular 3D processing software, yet converting 3DGS scenes into point clouds is a complex challenge. In this work we introduce 3DGS-to-PC, a flexible and highly customisable framework that is capable of transforming 3DGS scenes into dense, high-accuracy point clouds. We sample points probabilistically from each Gaussian as a 3D density function. We additionally threshold new points using the Mahalanobis distance to the Gaussian centre, preventing extreme outliers. The result is a point cloud that closely represents the shape encoded into the 3D Gaussian scene. Individual Gaussians use spherical harmonics to adapt colours depending on…
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