Learning Part-based Templates from Large Collections of 3D Shapes
Venue
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings, vol. 32, no. 4 (2013), 70:1-70:12
Publication Year
2013
Authors
Vladimir Kim, Wilmot Li, Niloy Mitra, Siddhartha Chaudhuri, Stephen DiVerdi, Thomas Funkhouser
BibTeX
Abstract
As large repositories of 3D shape collections continue to grow, understanding the
data, especially encoding the inter-model similarity and their variations, is of
central importance. For example, many data-driven approaches now rely on access to
semantic segmentation information, accurate inter-model point-to-point
correspondence, and deformation models that characterize the model collections.
Existing approaches, however, are either supervised requiring manual labeling; or
employ super-linear matching algorithms and thus are unsuited for analyzing large
collections spanning many thousands of models. We propose an automatic algorithm
that starts with an initial template model and then jointly optimizes for part
segmentation, point-to-point surface correspondence, and a compact deformation
model to best explain the input model collection. As output, the algorithm produces
a set of probabilistic part-based templates that groups the original models into
clusters of models capturing their styles and variations. We evaluate our algorithm
on several standard datasets and demonstrate its scalability by analyzing much
larger collections of up to thousands of shapes.
