Consensus Surfaces for Modeling 3D Objects from Multiple Range Images

Abstract

In this paper, we present a robust method for creating a triangulated surface mesh from multiple range images. Our method merges a set of range images into a volumetric implicit-surface representation which is converted to a surface mesh using a variant of the marching-cubes algorithm. Unlike previous techniques based on implicit-surface representations, our method estimates the signed distance to the object surface by finding a consensus of locally coherent observations of the surface. We call this method the consensus-surface algorithm. This algorithm effectively eliminates many of the troublesome effects of noise and extraneous surface observations without sacrificing the accuracy of the resulting surface. We utilize octrees to represent volumetric implicit surfaces—effectively reducing the computation and memory requirements of the volumetric representation without sacrificing accuracy of the resulting surface. We present results which demonstrate that our consensus-surface algorithm can construct accurate geometric models from rather noisy input range data.