  High-level shape understanding and technique evaluation on large repositories
of 3D shapes often benefit from additional information known about the shapes.
One example of such information is the semantic segmentation of a shape into
functional or meaningful parts. Generating accurate segmentations with
meaningful segment boundaries is, however, a costly process, typically
requiring large amounts of user time to achieve high quality results. In this
paper we present an active learning framework for large dataset segmentation,
which iteratively provides the user with new predictions by training new models
based on already segmented shapes. Our proposed pipeline consists of three
novel components. First, we a propose a fast and relatively accurate
feature-based deep learning model to provide dataset-wide segmentation
predictions. Second, we propose an information theory measure to estimate the
prediction quality and for ordering subsequent fast and meaningful shape
selection. Our experiments show that such suggestive ordering helps reduce
users time and effort, produce high quality predictions, and construct a model
that generalizes well. Finally, we provide effective segmentation refinement
features to help the user quickly correct any incorrect predictions. We show
that our framework is more accurate and in general more efficient than
state-of-the-art, for massive dataset segmentation with while also providing
consistent segment boundaries.
