Chen, Y and Cipolla, R (2009) Learning shape priors for single view reconstruction. 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009. pp. 1425-1432.Full text not available from this repository.
In this paper, we aim to reconstruct free-from 3D models from a single view by learning the prior knowledge of a specific class of objects. Instead of heuristically proposing specific regularities and defining parametric models as previous research, our shape prior is learned directly from existing 3D models under a framework based on the Gaussian Process Latent Variable Model (GPLVM). The major contributions of the paper include: 1) a probabilistic framework for prior-based reconstruction we propose, which requires no heuristic of the object, and can be easily generalized to handle various categories of 3D objects, and 2) an attempt at automatic reconstruction of more complex 3D shapes, like human bodies, from 2D silhouettes only. Qualitative and quantitative experimental results on both synthetic and real data demonstrate the efficacy of our new approach. ©2009 IEEE.
|Divisions:||Div F > Machine Intelligence|
|Depositing User:||Unnamed user with email firstname.lastname@example.org|
|Date Deposited:||15 Dec 2015 13:15|
|Last Modified:||11 Feb 2016 22:38|