denfense_seminar_flyer_guoxiang_zhang.pdf
denfense_seminar_flyer_guoxiang_zhang.pptx
Final disseration free PDF (full thesis http://escholarship.org/uc/item/1p72d4xt#metrics)
Electrical Engineering and Computer Science
Ph.D. Dissertation Defense
Towards Optimal 3D Reconstruction and Semantic Mapping
Guoxiang Zhang
Electrical Engineering and Computer Science
University of California, Merced
Schedule
Date: 05/06/2021
Time: 9:00 am
10:00 am
Zoom Link: https://ucmerced.zoom.us/j/6404390317
More Information
Guoxiang Zhang
gzhang8@ucmerced.edu
Faculty Advisor:
YangQuan Chen ychen53@ucmerced.edu
Abstract:
3D reconstruction and semantic mapping are of great importance for many tasks and applications, such as
consumer robots, augmented reality, and autonomous vehicles. Despite the drastic advancements in
solving the 3D reconstruction problem, it is still challenging to reconstruct accurate 3D models and create
semantic maps. Within this dissertation, contributions are made to take steps closer towards optimal 3D
reconstruction and semantic mapping. First, we introduce a novel 3D reconstruction system that corrects
surface loops with sparse feature based bundle adjustment. In the system, fast 3D surface based loop
detection is done by a GPU accelerated random sample consensus algorithm (RANSAC) with optimized
randomness supported by fractional calculus. Then, to solve a low precision problem in surface loop
detection, an online method for loop sifting is proposed for real time feedback to the users. For the best
3D reconstruction performance, an offline method for loop sifting and majorization is also proposed. To
overcome the difficulty in collecting ground truth data for evaluating 3D mapping systems, we propose
dense map posterior (DMP) as a metric for 3D reconstruction and mapping evaluation that can work
without any costly ground truth data. Finally, a simple and effective real time 3D semantic mapping
method is proposed. Besides, a benchmark suite for semantic mapping evaluation is presented.
Biography: Guoxiang Zhang is a Ph.D. Candidate at the University of California, Merced. Before joining UC Merced, he received his M.S. degree and B.E. degree from Xidian University. His research centers around 3D scene reconstruction and understanding. His current research interests are visual simultaneous localization and mapping, fractional order calculus, and 3D semantic mapping.
Last updated 4/25/2021 by Professor YangQuan Chen.
