M.S. Thesis Defense Jie Zhang Dense Point Cloud Extraction from Oblique Imagery Friday, 12 July 2013, 2:00 pm Carlson Bldg. 1275

M.S. Thesis Defense

Jie Zhang

Dense Point Cloud Extraction from Oblique Imagery

Advisor: Dr. John Kerekes

Friday, 12 July 2013, 2:00 pm 

Carlson Bldg. 1275

Abstract 

The automated creation of 3D urban models can be aided by the automatic extraction of dense point clouds from airborne imagery. The more dense the point clouds, the easier the modeling and the higher the accuracy. In this thesis, a modified workflow for the automated extraction of dense point cloud from oblique images is proposed and tested.

The basic workflow was established by previous research at the Rochester Institute of Technology (RIT) for point cloud extraction from nadir images. For oblique images, a first modification is proposed in the feature detection part by replacing the Scale-Invariant Feature Transform (SIFT) algorithm with the Affine Scale-Invariant Feature Transform (ASIFT) algorithm. After that, in order to realize a very dense point cloud, the Semi-Global Matching (SGM) algorithm is implemented in the second modification to compute the disparity map from a stereo image pair, which can then be used to reproject pixels back to a point cloud. A noise removal step is added in the third modification. At the end, an accuracy assessment reveals that this modified workflow works well and a very dense point cloud can be extracted from only two oblique images with slightly higher accuracy.