Volumetric Model Reconstruction from Unrestricted Camera Views Based on the Photo-consistency of 3D Voxel Mask

This project aims to develop a three-dimensional (3D) model reconstruction system using images acquired from a mobile camera. It consists of four major steps: camera calibration, volumetric model reconstruction, surface modeling and texture mapping. A novel online scale factor estimation is developed to enhance the accuracy of the coplanar camera calibration. For the volumetric modeling, the voting-based shape-from-silhouette first generates a coarse model, which is then refined by the photo-consistency check using the novel 3D voxel mask. Our scheme can handle concave surface in a sophisticated way. Finally, the surface model is formed with the original images mapped. 3D models of some test objects are presented.     

Automatic urban modeling using volumetric reconstruction with surface graph cuts

The demand for 3D city-scale models has been significantly increased due to the proliferation of urban planning, city navigation, and virtual reality applications. We present an approach to automatically reconstruct buildings densely spanning a large urban area. Our method takes as input calibrated aerial images and available GIS meta-data. Our computational pipeline computes a per-building 2.5D volumetric reconstruction by exploiting photo-consistency where it is highly sampled amongst the aerial images. Our building surface graph cut method overcomes errors of occlusion, geometry, and calibration in order to stitch together aerial images and yield a visually coherent texture-mapped result. Our comparisons show similar quality to the manually modeled buildings of Google Earth, and show improvements over naive texture mapping and over space-carving methods. We have tested our algorithms with a 12 sq km area of Boston, MA (USA), using 4667 images (i.e., 280 GB of raw image data) and producing 1785 buildings.