Indoor Scene Reconstruction from Sets of Noisy Range Images

Abstract

This paper describes a system for building 3D models of indoor scenes from sets of noisy laser range images. It addresses several important aspects of this problem, namely, preprocessing, which includes image segmentation and planar model fitting; view registration, which is the method of determining the rigid transformation that describes the relative pose of the camera platform between views; and reconstruction, which is the subsequent integration or fusion of separate range images into a single 3D model. Our proposed strategy is to use a statistical sensor model. We thus account for noise properties of the data at each stage in the reconstruction process, which produces reliable results even in the presence of significant measurement noise. We give an empirical analysis of a plane-based registration method and present results using real range data that demonstrate the performance of the entire reconstruction system.