Abstract:

Several techniques have been developed in research and industry for computing 3D geometry from sets of aligned range images. Recent work has shown that volumetric methods are robust to scanner noise and alignment uncertainty and provide good quality, water-tight models. However, these methods suffer from limited resolution, large memory requirements and long processing times, and they produce excessively large triangle models. In this report, we propose a new volumetric method for computing geometry from range data that: 1) computes distances directly from range images rather than from range surfaces, 2) generates an Adaptively Sampled Distance Field (ADF) rather than a distance volume or a 3-color octree, resulting in a significant savings in memory and distance computations, 3) provides an intuitive interface for manually correcting the generated ADF, and 4) generates optimal triangle models (with fewer triangles in flat regions and more triangles where needed to represent surface detail) from the generated ADF octree using a fast new triangulation method.