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

arXiv: https://arxiv.org/abs/2504.11451 · HTML: https://mlsanity.com/paper/2504.11451/

## Key facts

- Cited by 95 papers in the map (data through 14 Oct 2026).
- Builds on 68 of its 74 references that are in the map.
- First posted on arXiv 15 Apr 2025.

## Builds on (most cited first, 30 of 68)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022)
- [KPConv: Flexible and Deformable Convolution for Point Clouds](https://mlsanity.com/paper/1904.08889/) (2019)
- [Grounded Language-Image Pre-training](https://mlsanity.com/paper/2112.03857/) (2021)
- [Structured 3D Latents for Scalable and Versatile 3D Generation](https://mlsanity.com/paper/2412.01506/) (2024)
- [SAPIEN: A SimulAted Part-based Interactive ENvironment](https://mlsanity.com/paper/2003.08515/) (2020)
- [OpenScene: 3D Scene Understanding with Open Vocabularies](https://mlsanity.com/paper/2211.15654/) (2022)
- [LERF: Language Embedded Radiance Fields](https://mlsanity.com/paper/2303.09553/) (2023)
- [PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding](https://mlsanity.com/paper/1812.02713/) (2018)
- [PointCLIP: Point Cloud Understanding by CLIP](https://mlsanity.com/paper/2112.02413/) (2021)
- [PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies](https://mlsanity.com/paper/2206.04670/) (2022)
- [LangSplat: 3D Language Gaussian Splatting](https://mlsanity.com/paper/2312.16084/) (2023)
- [Gaussian Grouping: Segment and Edit Anything in 3D Scenes](https://mlsanity.com/paper/2312.00732/) (2023)
- [Feature 3DGS: Supercharging 3D Gaussian Splatting to Enable Distilled Feature Fields](https://mlsanity.com/paper/2312.03203/) (2023)
- [Point-Voxel CNN for Efficient 3D Deep Learning](https://mlsanity.com/paper/1907.03739/) (2019)
- [ABC: A Big CAD Model Dataset For Geometric Deep Learning](https://mlsanity.com/paper/1812.06216/) (2018)
- [PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation](https://mlsanity.com/paper/2004.01658/) (2020)
- [OpenMask3D: Open-Vocabulary 3D Instance Segmentation](https://mlsanity.com/paper/2306.13631/) (2023)
- [SoftGroup for 3D Instance Segmentation on Point Clouds](https://mlsanity.com/paper/2203.01509/) (2022)
- [PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning](https://mlsanity.com/paper/2211.11682/) (2022)
- [SAM3D: Segment Anything in 3D Scenes](https://mlsanity.com/paper/2306.03908/) (2023)
- [Open3DIS: Open-Vocabulary 3D Instance Segmentation with 2D Mask Guidance](https://mlsanity.com/paper/2312.10671/) (2023)
- [PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models](https://mlsanity.com/paper/2212.01558/) (2022)

## Built on by (most cited first, 30 of 95)

- [Efficient Part-level 3D Object Generation via Dual Volume Packing](https://mlsanity.com/paper/2506.09980/) (2025)
- [Ultra3D: Efficient and High-Fidelity 3D Generation with Part Attention](https://mlsanity.com/paper/2507.17745/) (2025)
- [X-Part: high fidelity and structure coherent shape decomposition](https://mlsanity.com/paper/2509.08643/) (2025)
- [Puppeteer: Rig and Animate Your 3D Models](https://mlsanity.com/paper/2508.10898/) (2025)
- [PhysX-3D: Physical-Grounded 3D Asset Generation](https://mlsanity.com/paper/2507.12465/) (2025)
- [IGGT: Instance-Grounded Geometry Transformer for Semantic 3D Reconstruction](https://mlsanity.com/paper/2510.22706/) (2025)
- [PartNeXt: A Next-Generation Dataset for Fine-Grained and Hierarchical 3D Part Understanding](https://mlsanity.com/paper/2510.20155/) (2025)
- [Particulate: Feed-Forward 3D Object Articulation](https://mlsanity.com/paper/2512.11798/) (2025)
- [iTACO: Interactable Digital Twins of Articulated Objects from Casually Captured RGBD Videos](https://mlsanity.com/paper/2506.08334/) (2025)
- [WorldGen: From Text to Traversable and Interactive 3D Worlds](https://mlsanity.com/paper/2511.16825/) (2025)
- [PartSAM: A Scalable Promptable Part Segmentation Model Trained on Native 3D Data](https://mlsanity.com/paper/2509.21965/) (2025)
- [Rectified Point Flow: Generic Point Cloud Pose Estimation](https://mlsanity.com/paper/2506.05282/) (2025)
- [GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation](https://mlsanity.com/paper/2508.14036/) (2025)
- [HY3D-Bench: Generation of 3D Assets](https://mlsanity.com/paper/2602.03907/) (2026)
- [PhysWorld: From Real Videos to World Models of Deformable Objects via Physics-Aware Demonstration Synthesis](https://mlsanity.com/paper/2510.21447/) (2025)
- [SegviGen: Repurposing 3D Generative Model for Part Segmentation](https://mlsanity.com/paper/2603.16869/) (2026)
- [Feedforward 3D Editing Learns from Semantic-Part Transformation](https://mlsanity.com/paper/2605.27351/) (2026)
- [PhysChoreo: Physics-Controllable Video Generation with Part-Aware Semantic Grounding](https://mlsanity.com/paper/2511.20562/) (2025)
- [UniPart: Part-Level 3D Generation with Unified 3D Geom-Seg Latents](https://mlsanity.com/paper/2512.09435/) (2025)
- [PhysForge: Generating Physics-Grounded 3D Assets for Interactive Virtual World](https://mlsanity.com/paper/2605.05163/) (2026)
- [MeshMosaic: Scaling Artist Mesh Generation via Local-to-Global Assembly](https://mlsanity.com/paper/2509.19995/) (2025)
- [Part-X-MLLM: Part-aware 3D Multimodal Large Language Model](https://mlsanity.com/paper/2511.13647/) (2025)
- [GaussianFluent: Gaussian Simulation for Dynamic Scenes with Mixed Materials](https://mlsanity.com/paper/2601.09265/) (2026)
- [Affogato: Open-Vocabulary Affordance Grounding with Automated Data Generation at Scale](https://mlsanity.com/paper/2506.12009/) (2025)
- [PatchAlign3D: Local Feature Alignment for Dense 3D Shape Understanding](https://mlsanity.com/paper/2601.02457/) (2026)
- [Feed-Forward 3D Scene Modeling: A Problem-Driven Perspective](https://mlsanity.com/paper/2604.14025/) (2026)
- [Residual Primitive Fitting of 3D Shapes with SuperFrusta](https://mlsanity.com/paper/2512.09201/) (2025)
- [MonoArt: Progressive Structural Reasoning for Monocular Articulated 3D Reconstruction](https://mlsanity.com/paper/2603.19231/) (2026)
- [SIMART: Decomposing Monolithic Meshes into Sim-ready Articulated Assets via MLLM](https://mlsanity.com/paper/2603.23386/) (2026)
- [PartDiffuser: Part-wise 3D Mesh Generation via Discrete Diffusion](https://mlsanity.com/paper/2511.18801/) (2025)

## Abstract (arXiv)

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/