Learning-based Geometric Image Matching with Modern Deep Learning Techniques: A Survey

PhD Qualifying Examination


Title: "Learning-based Geometric Image Matching with Modern Deep Learning 
Techniques: A Survey"

by

Mr. Zixin LUO


Abstract:

Geometric image matching targets to establish reliable correspondences that are 
geometrically consistent across images under different perspective or lighting 
conditions, which builds a crucial foundation for a broad range of computer 
vision tasks, including panorama stitching, visual localization, 
Structure-from-Motion (SfM), Simultaneous Localization and Mapping (SLAM), 
Augmented Reality (AR) and 3D reconstruction. In the past decade, hand-crafted 
local features and engineered geometric matchers have been widely used as the 
de-facto standard, upon which many popular applications are developed and 
already in commercial use in real scenarios. With the emerging of deep 
learning, a great amount of effort has been recently spent on integrating the 
image matching pipeline into modern neural network architectures in a 
differentiable manner. In this survey, we will first review the recent 
achievements on learning-based image matching techniques, and then elaborate 
the methods we have proposed that give rise to state-of-the-art results on 
several important benchmark datasets. More specifically, we decompose the 
learning-based image matching into four sub-modules, including 1) a keypoint 
detector and 2) a keypoint descriptor for local feature extraction. Next, 3) an 
image retrieval system that shortlists the matching candidates from a large 
image collection and finally, 4) a feature matcher that computes the geometry 
model. To facilitate the above research, we further present a data generation 
pipeline that offers accurate and rich geometric learning labels automatically 
from off-the-shelf 3D reconstructions. Through extensive evaluations, we 
demonstrate the superiority of the integration of learning-based image matching 
methods in real applications, and show great potential for future improvements 
in this area.


Date:			Tuesday, 20 August 2019

Time:                  	2:00pm - 4:00pm

Venue:                  Room 5501
                         Lifts 25/26

Committee Members:	Prof. Long Quan (Supervisor)
 			Prof. Chiew-Lan Tai (Chairperson)
 			Dr. Qifeng Chen
 			Dr. Pedro Sander


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