Voxel-Based Indoor Reconstruction From HoloLens Triangle Meshes

TL;DR

This paper introduces a voxel-based method for automatic indoor reconstruction from triangle meshes obtained by devices like HoloLens, enabling detailed and semantic 3D indoor models without assuming Manhattan World constraints.

Contribution

The novel voxel-based approach allows for flexible, automatic indoor reconstruction from unstructured 3D data, including semantic labeling and geometry refinement, without relying on planar assumptions.

Findings

Effective room detection through voxel segmentation

Semantic labeling of walls, openings, and interior objects

Geometry refinement improves model accuracy

Abstract

Current mobile augmented reality devices are often equipped with range sensors. The Microsoft HoloLens for instance is equipped with a Time-Of-Flight (ToF) range camera providing coarse triangle meshes that can be used in custom applications. We suggest to use the triangle meshes for the automatic generation of indoor models that can serve as basis for augmenting their physical counterpart with location-dependent information. In this paper, we present a novel voxel-based approach for automated indoor reconstruction from unstructured three-dimensional geometries like triangle meshes. After an initial voxelization of the input data, rooms are detected in the resulting voxel grid by segmenting connected voxel components of ceiling candidates and extruding them downwards to find floor candidates. Semantic class labels like 'Wall', 'Wall Opening', 'Interior Object' and 'Empty Interior' are then assigned to the room voxels in-between ceiling and floor by a rule-based voxel sweep algorithm. Finally, the geometry of the detected walls and their openings is refined in voxel representation. The proposed approach is not restricted to Manhattan World scenarios and does not rely on room surfaces being planar.