Name That Part: 3D Part Segmentation and Naming
We address semantic 3D part segmentation: decomposing objects into parts with meaningful names. While datasets exist with part annotations, their definitions are inconsistent across datasets, limiting robust training. Previous methods produce unlabeled decompositions or retrieve single parts without complete shape annotations. We propose ALIGN-Parts, which formulates part naming as a direct set alignment task. Our method decomposes shapes into partlets - implicit 3D part representations - matched to part descriptions via bipartite assignment. We combine geometric cues from 3D part fields, appearance from multi-view vision features, and semantic knowledge from language-model-generated affordance descriptions. Text-alignment loss ensures partlets share embedding space with text, enabling a theoretically open-vocabulary matching setup, given sufficient data. Our efficient and novel, one-shot, 3D part segmentation and naming method finds applications in several downstream tasks, including serving as a scalable annotation engine. As our model supports zero-shot matching to arbitrary descriptions and confidence-calibrated predictions for known categories, with human verification, we create a unified ontology that aligns PartNet, 3DCoMPaT++, and Find3D, consisting of 1,794 unique 3D parts. We also show examples from our newly created Tex-Parts dataset. We also introduce 2 novel metrics appropriate for the named 3D part segmentation task.
694a10a1335742716e932284
Soumava Paul
694a10a1335742716e932285
Prakhar Kaushik
claimed_verified
2025-12-25T20:48:26.210Z
6446940217bdb138c95245ea
/avatars/ad6701804ca92abfb257edcaef89aa19.svg
Prakhar Kaushik
toshi2k2
user
toshi2k2
694a10a1335742716e932286
Ankit Vaidya
694a10a1335742716e932287
Anand Bhattad
694a10a1335742716e932288
Alan Yuille
Name That Part: 3D Part Segmentation and Naming
We address semantic 3D part segmentation: decomposing objects into parts with meaningful names. While datasets exist with part annotations, their definitions are inconsistent across datasets, limiting robust training. Previous methods produce unlabeled decompositions or retrieve single parts without complete shape annotations. We propose ALIGN-Parts, which formulates part naming as a direct set alignment task. Our method decomposes shapes into partlets - implicit 3D part representations - matched to part descriptions via bipartite assignment. We combine geometric cues from 3D part fields, appearance from multi-view vision features, and semantic knowledge from language-model-generated affordance descriptions. Text-alignment loss ensures partlets share embedding space with text, enabling a theoretically open-vocabulary matching setup, given sufficient data. Our efficient and novel, one-shot, 3D part segmentation and naming method finds applications in several downstream tasks, including serving as a scalable annotation engine. As our model supports zero-shot matching to arbitrary descriptions and confidence-calibrated predictions for known categories, with human verification, we create a unified ontology that aligns PartNet, 3DCoMPaT++, and Find3D, consisting of 1,794 unique 3D parts. We also show examples from our newly created Tex-Parts dataset. We also introduce 2 novel metrics appropriate for the named 3D part segmentation task.
ALIGN-Parts addresses semantic 3D part segmentation by aligning implicit 3D part representations with part descriptions using geometric, appearance, and semantic cues, supporting open-vocabulary part naming and creating a unified ontology for multiple datasets.
3D part segmentation part naming set alignment partlets bipartite assignment 3D part fields multi-view vision features language-model-generated affordance descriptions text-alignment loss zero-shot matching confidence-calibrated predictions unified ontology Tex-Parts dataset