In this paper we present an efficient and general approach to computing and integrating 3D distance fields directly from multiple range images. We compute normal and confidence values directly from a 2D range image. We then approximate 3D Euclidean distance by correcting the line-of-sight distance. To integrate multiple scans, we efficiently transform voxels of the target distance field to each scan's local coordinate system, then update voxels with computed distance and confidence values. Finally, we extract an isosurface from the weighted distance field using the marching cubes algorithm. We extend the same idea to the assignment of weighted colors or texture coordinates to the reconstructed model. Experiments show that our approach is fast, has reasonable storage requirements, and can produce high-quality surfaces from multiple range scans.