  We introduce AdaCoSeg, a deep neural network architecture for adaptive
co-segmentation of a set of 3D shapes represented as point clouds. Differently
from the familiar single-instance segmentation problem, co-segmentation is
intrinsically contextual: how a shape is segmented can vary depending on the
set it is in. Hence, our network features an adaptive learning module to
produce a consistent shape segmentation which adapts to a set. Specifically,
given an input set of unsegmented shapes, we first employ an offline
pre-trained part prior network to propose per-shape parts. Then, the
co-segmentation network iteratively and} jointly optimizes the part labelings
across the set subjected to a novel group consistency loss defined by matrix
ranks. While the part prior network can be trained with noisy and
inconsistently segmented shapes, the final output of AdaCoSeg is a consistent
part labeling for the input set, with each shape segmented into up to (a
user-specified) K parts. Overall, our method is weakly supervised, producing
segmentations tailored to the test set, without consistent ground-truth
segmentations. We show qualitative and quantitative results from AdaCoSeg and
evaluate it via ablation studies and comparisons to state-of-the-art
co-segmentation methods.
