Shape decomposition with supervised learning Relatedly, some methods use primitive decompositions to formulate self-supervised losses that augment training to improve few-shot semantic labeling (Gadelha et al., 2020 Sharma et al., 2021).
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Without annotations, these approaches rely on global reconstruction-based losses, resulting in decompositions that well-represent coarse structures, but often ignore fine-grained regions of interest. These methods can train on 3D shapes that lack annotations and produce segmentations with paired correspondences across different shape instances. Superquadrics (Paschalidou et al., 2019),Ĭonvex solids (Deng et al., 2020 Chen et al., 2019a),Īnd more general local neural functions (Paschalidou et al., 2021 Kawana et al., 2020 Genova et al., 2019 Chen et al., 2019b). Methods differ by the type of primitive used, for instance cuboids (Tulsiani et al., 2017 Sun et al., 2019 Yang and Chen, 2021), We then demonstrate that SHRED’s decompositions can be treated as fine-grained part instance segmentations that outperform comparison methods.įinally, we evaluate how SHRED can improve fine-grained semantic segmentation, using the output of SHRED as the input to a method that learns to semantically label shape regions, and find that using SHRED results in the best performance.Ī great body of recent research has been devoted to learning methods that aim to coarsely approximate 3D shapes with a union of primitive structures. We find that SHRED produces better region decompositions than baseline methods, for both in-domain and out-domain categories.Īnalyzing the trade-off between decomposition quality and granularity, we vary SHRED’s merge-threshold to create a Pareto frontier of solutions that strictly dominates all comparisons. We compare SHRED against other shape segmentation methods, including learned and non-learned approaches, that operate both globally and locally. We train a version of SHRED on three data abundant categories of PartNet: chairs, lamps and storage furniture ( in-domain shapes), and evaluate its ability to produce region decompositions for test-set in-domain and out-domain shapes. Part instance segmentation and few-shot fine-grained semantic segmentation whenĬombined with methods that learn to label shape regions. Finally, we demonstrate that SHRED is useful forĭownstream applications, out-performing all baselines on zero-shot fine-grained Ground-truth annotations compared with baseline methods, at any desiredĭecomposition granularity. Hyperparameter, we show that SHRED produces segmentations that better respect SHRED with fine-grained segmentations from PartNet using its merge-threshold Segmentations for categories not seen during training.
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Independently and locally, allowing SHRED to generate high-quality
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Three decomposition operations: splitting regions, fixing the boundariesīetween regions, and merging regions together. Segmentation that approximates fine-grained part instances. SHRED takes aģD point cloud as input and uses learned local operations to produce a We present SHRED, a method for 3D SHape REgion Decomposition.