  Parts represent a basic unit of geometric and semantic similarity across
different objects. We argue that part knowledge should be composable beyond the
observed object classes. Towards this, we present 3D Compositional Zero-shot
Learning as a problem of part generalization from seen to unseen object classes
for semantic segmentation. We provide a structured study through benchmarking
the task with the proposed Compositional-PartNet dataset. This dataset is
created by processing the original PartNet to maximize part overlap across
different objects. The existing point cloud part segmentation methods fail to
generalize to unseen object classes in this setting. As a solution, we propose
DeCompositional Consensus, which combines a part segmentation network with a
part scoring network. The key intuition to our approach is that a segmentation
mask over some parts should have a consensus with its part scores when each
part is taken apart. The two networks reason over different part combinations
defined in a per-object part prior to generate the most suitable segmentation
mask. We demonstrate that our method allows compositional zero-shot
segmentation and generalized zero-shot classification, and establishes the
state of the art on both tasks.
