StructureTransfer: A Scene Parsing Framework via Graph Matching for Images and Point Clouds

Scene parsing is to densely label the pixels in an image with the semantic categories. In this thesis, we present a scene parsing framework which can work on both images and point clouds. To this end, we develop two separate pipelines for images and point clouds. For point clouds, a coarse segmentation is implemented to obtain an initial distribution for the objects. For images, superpixel segmentation is implemented and StructureTransfer is carried out. StructureTransfer is a model to find similar regions across scenes. The two pipelines converge at the inference step. Several novel potentials, representing point cloud constraints and StructureTransfer scores, are introduced into a traditional Markov Random Field (MRF) for the inference. The parsing accuracy of the proposed method is close to state-of-the-art algorithms on images. With the point clouds, the accuracy is significantly enhanced. The proposed framework shows remarkable prospect in real-world applications.

Name thesis:degree_name

Master of Science (MSc)

Discipline thesis:degree_discipline

Geomatics Engineering

Grantor dc:publisher.institution

Graduate Studies

Year dc:date.issued

2016

Author dc:creator

• Yu, Tianshu

Advisor dc:contributor.advisor

• Wang, Ruisheng

Committee members dc:contributor.committeemember

• Wang, Ruisheng
• Wang, Xin
• Hassan, Quazi

• Computer Science
• Robotics

Statement dc:rights

• University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.

Language dc:language.iso

eng

DOI

http://dx.doi.org/10.11575/PRISM/27906

OAI identifier oai:identifier

oai:ucalgary.scholaris.ca:11023/3072