A Statistical Geometric Framework for Reconstruction of Scene Models

This paper addresses the problem of reconstructing surface models of indoor scenes from sparse 3D scene structure captured from N camera views. Sparse 3D measurements of real scenes are readily estimated from image sequences using structure-from-motion techniques. Currently there is no general method for reconstruction of 3D models of arbitrary scenes from sparse data. We previously introduced an algorithm for recursive integration of sparse 3D structure to obtain a consistent model. In this paper we focus on incorporating uncertainty information into model to achieve reliable reconstruction of real-scenes in the presence of noise. A statistical geometric framework is described that provides a unified approach to probabilistic scene reconstruction from sparse or even dense 3D scene structure.

• Type: Conference
• Year: 2000
• Where: BMVC
• Authors: Anastasios Manessis, Adrian Hilton, Philip F. McLauchlan, Phil Palmer
• Keywords: BMVC 2000, Computer Vision, Sparse, Sparse 3D Measurements, Sparse 3D Scene