PARTFIELD: Learning 3D Feature Fields for Part Segmentation and Beyond

• URL: http://arxiv.org/abs/2504.11451v1
• Date: Tue, 15 Apr 2025 17:58:16 GMT
• Title: PARTFIELD: Learning 3D Feature Fields for Part Segmentation and Beyond
• Authors: Minghua Liu, Mikaela Angelina Uy, Donglai Xiang, Hao Su, Sanja Fidler, Nicholas Sharp, Jun Gao,
• Abstract summary: PartField is a feedforward approach for learning part-based 3D features.<n>PartField is up to 20% more accurate and often orders of magnitude faster than other recent class-agnostic part-segmentation methods.
• Score: 70.95930509071451
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose PartField, a feedforward approach for learning part-based 3D features, which captures the general concept of parts and their hierarchy without relying on predefined templates or text-based names, and can be applied to open-world 3D shapes across various modalities. PartField requires only a 3D feedforward pass at inference time, significantly improving runtime and robustness compared to prior approaches. Our model is trained by distilling 2D and 3D part proposals from a mix of labeled datasets and image segmentations on large unsupervised datasets, via a contrastive learning formulation. It produces a continuous feature field which can be clustered to yield a hierarchical part decomposition. Comparisons show that PartField is up to 20% more accurate and often orders of magnitude faster than other recent class-agnostic part-segmentation methods. Beyond single-shape part decomposition, consistency in the learned field emerges across shapes, enabling tasks such as co-segmentation and correspondence, which we demonstrate in several applications of these general-purpose, hierarchical, and consistent 3D feature fields. Check our Webpage! https://research.nvidia.com/labs/toronto-ai/partfield-release/

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