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Title: SHRED: 3D Shape Region Decomposition with Learned Local Operations
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 finegrained semantic segmentation when combined with methods that learn to label shape regions.  more » « less
Award ID(s):
1941808
PAR ID:
10403485
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
ACM Transactions on Graphics
Volume:
41
Issue:
6
ISSN:
0730-0301
Page Range / eLocation ID:
1 to 11
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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