# SHRED: 3D Shape Region Decomposition with Learned Local Operations

Source: [SHRED: 3D Shape Region Decomposition with Learned Local Operations](<https://www.sota2.com/research/paper/68fa6d8b1e209131cea3a80a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:55.410Z

Authors: R. Kenny Jones, Aalia Habib, Daniel Ritchie

Published: 2022-06-07T17:55:15.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2206.03480>)

[PDF](<https://arxiv.org/pdf/2206.03480.pdf>)