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.
@article{arxiv.2509.16423,
title = {3D Gaussian Flats: Hybrid 2D/3D Photometric Scene Reconstruction},
author = {Maria Taktasheva and Lily Goli and Alessandro Fiorini and Zhen Li and Daniel Rebain and Andrea Tagliasacchi},
journal= {arXiv preprint arXiv:2509.16423},
year = {2025}
}