Automatic reconstruction of textured 3D models

Abstract

This paper describes a system for automatic mapping and generation of textured 3D models of indoor environments without user interaction. Our data acquisition system is based on a Segway RMP platform which allows us to automatically acquire large amounts of textured 3D scans in a short amount of time. The first data processing step is registration and mapping. We propose a probabilistic, non-rigid registration method that incorporates statistical sensor models and surface prior distributions to optimize alignment and the reconstructed surface at the same time. Second, in order to fuse multiple scans and to reconstruct a consistent 3D surface representation, we incorporate a volumetric surface reconstruction method based on a oriented point. For the final step of texture reconstruction, we present a novel method to automatically generate blended textures from multiple images and multiple scans which are mapped onto the 3D model for photo-realistic visualization. We conclude our report with results from a large-scale, real-world experiment. The most significant contribution of this research is a functional system that covers all steps required to automatically reconstruct textured 3D models of large indoor environments.