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

**Authors:** Maria Taktasheva, Lily Goli, Alessandro Fiorini,  Zhen,  Li, Daniel Rebain, Andrea Tagliasacchi  
**arXiv:** [2509.16423](https://arxiv.org/abs/2509.16423)  
**Date:** 2025-09-19  
**Summary page:** https://www.summarizepaper.com/fr/paper/2509.16423v1/

## 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.