  We present SHRED, a method for 3D SHape REgion Decomposition. SHRED takes a
3D point cloud as input and uses learned local operations to produce a
segmentation that approximates fine-grained part instances. We endow SHRED with
three decomposition operations: splitting regions, fixing the boundaries
between regions, and merging regions together. Modules are trained
independently and locally, allowing SHRED to generate high-quality
segmentations for categories not seen during training. We train and evaluate
SHRED with fine-grained segmentations from PartNet; using its merge-threshold
hyperparameter, we show that SHRED produces segmentations that better respect
ground-truth annotations compared with baseline methods, at any desired
decomposition granularity. Finally, we demonstrate that SHRED is useful for
downstream applications, out-performing all baselines on zero-shot fine-grained
part instance segmentation and few-shot fine-grained semantic segmentation when
combined with methods that learn to label shape regions.
