$\mathrm{Tri^{2}plane}$: Advancing Neural Implicit Surface Reconstruction for Indoor Scenes

Reconstructing 3D indoor scenes presents significant challenges, requiring models capable of inferring both planar surfaces and intricate details. Although recent methods can generate complete surfaces, they often struggle to simultaneously reconstruct low-texture regions and high-frequency details due to non-local effects. In this paper, we introduce a novel triangle-based triplane representation, named (tri$^{2}$plane), specifically designed to account for the diverse spatial feature distribution and information density of indoor environments. Our method begins by projecting point clouds onto three orthogonal planes, followed by 2D Delaunay triangulation. This representation enables adaptive encoding of low-texture and high-frequency regions by employing triangles of variable sizes. Moreover, we develop a dual tri$^{2}$ plane framework that incorporates both geometric and semantic information, significantly enhancing the reconstruction quality. We combine these key modules and evaluate our method on benchmark indoor scene datasets. The results unequivocally demonstrate the superiority of our proposed method over the state-of-the-art Occ-SDF. Specifically, our method achieves significant improvements over Occ-SDF, with margins of 1.3, 1.7, and 2.3 in F-score on the ScanNet, Tanks & Temples, and Replica datasets, respectively. To facilitate further research, we will make our code publicly available.