SHRED: 3D Shape Region Decomposition with Learned Local Operations

• Main Authors: Jones, R. Kenny, Habib, Aalia, Ritchie, Daniel
• Format: Journal Article
• Language: English
• Published: 07-06-2022
• Subjects: Computer Science - Computer Vision and Pattern Recognition; Computer Science - Graphics; Computer Science - Learning
• DOI: 10.48550/arxiv.2206.03480

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.