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OPD: Single-view 3D Openable Part Detection

Resource type
Thesis type
(Thesis) M.Sc.
Date created
2023-03-24
Authors/Contributors
Abstract
We address the task of predicting what parts of an object can open and how they move when they do so. The input is a single image of an object, and as output we detect what parts of the object can open, and the motion parameters describing the articulation of each openable part. To tackle this task, we create two datasets of 3D objects: OPDSynth based on synthetic objects, and OPDReal based on RGBD reconstructions of real objects. We then design OPDRCNN and OPDFormer, neural architectures that detect openable parts and predict their motion parameters. Our experiments show that this is a challenging task especially when considering generalization across object categories, and the limited amount of information in a single image. Our architectures outperform baselines and prior work especially for RGB image inputs.
Document
Extent
53 pages.
Identifier
etd22491
Copyright statement
Copyright is held by the author(s).
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: Xuan, Chang, Angel
Language
English
Member of collection
Download file Size
etd22491.pdf 23.05 MB

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