Fangning He, a PhD Candidate in the Lyles School of Civil Engineering, has been named as recipient of the 2016 Bilsland Dissertation Fellowship Award. The title of Fangning’s dissertation is "Dense Point Cloud Generation from Passive and Active Sensors."

Fangning’s current research deals with precise point cloud generation from passive and active remote sensing systems. In this regard, he has been developing a prototype system for the automated  recovery of the orientation parameters for a set of images that has been captured by low-cost digital cameras onboard Unmanned Aerial Vehicles (UAVs) as well as generation of dense 3D point clouds from this data. More specifically, the developed prototype system has several advantages, including: 1) it utilizes an improved orientation recovery for UAV flight missions in the presence of prior information regarding the platform trajectory; 2) it can handle imagery with repetitive texture; and 3) it adopts a modified semi-global dense matching technique, which optimizes the 3D reconstruction in object space, for image-based dense point cloud generation. In the meantime, to consider the complementary characteristics of data acquired from different remote sensing systems, he is also introducing procedures for the registration of image-based and laser-based point clouds. Such development will enhance the performance of existing dense image matching techniques as well as extraction/interpretation of available features in the imaged objects.

Fangning is advised by Professor Ayman Habib.