IM360: Textured Mesh Reconstruction for Large-scale Indoor Mapping with 360\textdegree\ Cameras

We propose a complete pipeline for indoor mapping using omnidirectional images, consisting of three key stages: (1) Spherical SfM, (2) Neural Surface Reconstruction, and (3) Texture Optimization.

We present a novel 3D reconstruction pipeline for 360° cameras for 3D mapping and rendering of indoor environments. Traditional Structure-from-Motion (SfM) methods may not work well in large-scale indoor scenes due to the prevalence of textureless and repetitive regions. To overcome these challenges, our approach (IM360) leverages the wide field of view of omnidirectional images and integrates the spherical camera model into every core component of the SfM pipeline. In order to develop a comprehensive 3D reconstruction solution, we integrate a neural implicit surface reconstruction technique to generate high-quality surfaces from sparse input data. Additionally, we utilize a mesh-based neural rendering approach to refine texture maps and accurately capture view-dependent properties by combining diffuse and specular components. We evaluate our pipeline on large-scale indoor scenes from the Matterport3D and Stanford2D3D datasets. In practice, IM360 demonstrate superior performance in terms of textured mesh reconstruction over SOTA. We observe accuracy improvements in terms of camera localization and registration as well as rendering high frequency details.

We provide a visual comparison between sparse matching perspective SfM and our proposed dense matching spherical SfM. In (a), ERP images are converted into a cubemap representation, after which feature matching is performed across all 36 possible image pairs, resulting in sparse and noisy correspondence matches. (b) demonstrates our approach, which directly finds dense and accurate correspondences on ERP images, thereby facilitating the construction of a detailed 3D structure.

By leveraging dense features on equirectangular projection (ERP) images, our method effectively finds correspondences in textureless regions, registers more camera poses, and reconstructs a greater number of 3D points.

Using accurately estimated camera poses from a 360-degree vision sensor, we can construct a geometric mesh from sparsely scanned, large-scale indoor datasets.

IM360: Textured Mesh Reconstruction for Large-scale Indoor Mapping with 360° Cameras

Annonymous

We propose a complete pipeline for indoor mapping using omnidirectional images, consisting of three key stages: (1) Spherical SfM, (2) Neural Surface Reconstruction, and (3) Texture Optimization.

Abstract

Results: Neural Surface Reconstruction

Results: Texture Optimization