3D Gaussian Flats: Hybrid 2D/3D Photometric Scene Reconstruction

• URL: http://arxiv.org/abs/2509.16423v2
• Date: Tue, 23 Sep 2025 00:47:20 GMT
• Title: 3D Gaussian Flats: Hybrid 2D/3D Photometric Scene Reconstruction
• Authors: Maria Taktasheva, Lily Goli, Alessandro Fiorini, Zhen Li, Daniel Rebain, Andrea Tagliasacchi,
• Abstract summary: We propose a novel hybrid 2D/3D representation that jointly optimize constrained planar (2D) Gaussians for modeling flat surfaces and freeform (3D) Gaussians for the rest of the scene.<n>Our end-to-end approach dynamically detects and refines planar regions, improving both visual fidelity and geometric accuracy.<n>It achieves state-of-the-art depth estimation on ScanNet++ and ScanNetv2, and excels at mesh extraction without overfitting to a specific camera model.
• Score: 62.84879632157956
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in radiance fields and novel view synthesis enable creation of realistic digital twins from photographs. However, current methods struggle with flat, texture-less surfaces, creating uneven and semi-transparent reconstructions, due to an ill-conditioned photometric reconstruction objective. Surface reconstruction methods solve this issue but sacrifice visual quality. We propose a novel hybrid 2D/3D representation that jointly optimizes constrained planar (2D) Gaussians for modeling flat surfaces and freeform (3D) Gaussians for the rest of the scene. Our end-to-end approach dynamically detects and refines planar regions, improving both visual fidelity and geometric accuracy. It achieves state-of-the-art depth estimation on ScanNet++ and ScanNetv2, and excels at mesh extraction without overfitting to a specific camera model, showing its effectiveness in producing high-quality reconstruction of indoor scenes.

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