# P3-SAM: Native 3D Part Segmentation

arXiv: https://arxiv.org/abs/2509.06784 · HTML: https://mlsanity.com/paper/2509.06784/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 39 references that are in the map.
- First posted on arXiv 8 Sep 2025.

## Builds on (most cited first, 30 of 33)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022)
- [Grounded Language-Image Pre-training](https://mlsanity.com/paper/2112.03857/) (2021)
- [Objaverse-XL: A Universe of 10M+ 3D Objects](https://mlsanity.com/paper/2307.05663/) (2023)
- [Point Transformer V3: Simpler, Faster, Stronger](https://mlsanity.com/paper/2312.10035/) (2023)
- [OpenScene: 3D Scene Understanding with Open Vocabularies](https://mlsanity.com/paper/2211.15654/) (2022)
- [PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding](https://mlsanity.com/paper/1812.02713/) (2018)
- [SAM3D: Segment Anything in 3D Scenes](https://mlsanity.com/paper/2306.03908/) (2023)
- [PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models](https://mlsanity.com/paper/2212.01558/) (2022)
- [Sonata: Self-Supervised Learning of Reliable Point Representations](https://mlsanity.com/paper/2503.16429/) (2025)
- [SAMPart3D: Segment Any Part in 3D Objects](https://mlsanity.com/paper/2411.07184/) (2024)
- [Segment3D: Learning Fine-Grained Class-Agnostic 3D Segmentation without Manual Labels](https://mlsanity.com/paper/2312.17232/) (2023)
- [SATR: Zero-Shot Semantic Segmentation of 3D Shapes](https://mlsanity.com/paper/2304.04909/) (2023)
- [OmniPart: Part-Aware 3D Generation with Semantic Decoupling and Structural Cohesion](https://mlsanity.com/paper/2507.06165/) (2025)
- [HoloPart: Generative 3D Part Amodal Segmentation](https://mlsanity.com/paper/2504.07943/) (2025)
- [Point-SAM: Promptable 3D Segmentation Model for Point Clouds](https://mlsanity.com/paper/2406.17741/) (2024)
- [Better Call SAL: Towards Learning to Segment Anything in Lidar](https://mlsanity.com/paper/2403.13129/) (2024)
- [SAMPro3D: Locating SAM Prompts in 3D for Zero-Shot Instance Segmentation](https://mlsanity.com/paper/2311.17707/) (2023)
- [Segment Any Mesh](https://mlsanity.com/paper/2408.13679/) (2024)
- [BANG: Dividing 3D Assets via Generative Exploded Dynamics](https://mlsanity.com/paper/2507.21493/) (2025)
- [Find Any Part in 3D](https://mlsanity.com/paper/2411.13550/) (2024)
- [PartDistill: 3D Shape Part Segmentation by Vision-Language Model Distillation](https://mlsanity.com/paper/2312.04016/) (2023)

## Built on by (most cited first, 30 of 43)

- [X-Part: high fidelity and structure coherent shape decomposition](https://mlsanity.com/paper/2509.08643/) (2025)
- [Particulate: Feed-Forward 3D Object Articulation](https://mlsanity.com/paper/2512.11798/) (2025)
- [Utonia: Toward One Encoder for All Point Clouds](https://mlsanity.com/paper/2603.03283/) (2026)
- [Hunyuan3D Studio: End-to-End AI Pipeline for Game-Ready 3D Asset Generation](https://mlsanity.com/paper/2509.12815/) (2025)
- [HY3D-Bench: Generation of 3D Assets](https://mlsanity.com/paper/2602.03907/) (2026)
- [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)
- [UniPart: Part-Level 3D Generation with Unified 3D Geom-Seg Latents](https://mlsanity.com/paper/2512.09435/) (2025)
- [ArtLLM: Generating Articulated Assets via 3D LLM](https://mlsanity.com/paper/2603.01142/) (2026)
- [Part-X-MLLM: Part-aware 3D Multimodal Large Language Model](https://mlsanity.com/paper/2511.13647/) (2025)
- [EmbodiedGen V2: An Agentic, Simulation-Ready 3D World Engine for Embodied AI](https://mlsanity.com/paper/2607.07459/) (2026)
- [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)
- [CubePart: An Open-Vocabulary Part-Controllable 3D Generator](https://mlsanity.com/paper/2605.28763/) (2026)
- [Instruct-Particulate: Scaling Feed-Forward 3D Object Articulation with Kinematic Control](https://mlsanity.com/paper/2606.14699/) (2026)
- [DragMesh: Interactive 3D Generation Made Easy](https://mlsanity.com/paper/2512.06424/) (2025)
- [Interact3D: Compositional 3D Generation of Interactive Objects](https://mlsanity.com/paper/2603.16085/) (2026)
- [MotionAnymesh: Physics-Grounded Articulation for Simulation-Ready Digital Twins](https://mlsanity.com/paper/2603.12936/) (2026)
- [UniPhysGen: Unified Physical Grounding for Simulation-Ready 3D Assets](https://mlsanity.com/paper/2607.13586/) (2026)
- [Mesh RAG: Retrieval Augmentation for Autoregressive Mesh Generation](https://mlsanity.com/paper/2511.16807/) (2025)
- [Universal 3D Shape Matching via Coarse-to-Fine Language Guidance](https://mlsanity.com/paper/2602.19112/) (2026)
- [EI-Part: Explode for Completion and Implode for Refinement](https://mlsanity.com/paper/2603.14021/) (2026)
- [PAR3D: A Unified 3D-MLLM with Part-Aware Representation for Scene Understanding](https://mlsanity.com/paper/2606.06485/) (2026)
- [UnfoldArt: Zero-Shot Recovery of Full Articulated 3D Objects from Text or Image](https://mlsanity.com/paper/2606.30608/) (2026)
- [EditVerse3D: High-Quality 3D Object Editing with Region-Aware Learning](https://mlsanity.com/paper/2607.07187/) (2026)
- [EditFlow3D: Automated Local Editing of 3D Assets with Trajectory Preservation](https://mlsanity.com/paper/2608.03179/) (2026)
- [S2AM3D: Scale-controllable Part Segmentation of 3D Point Clouds](https://mlsanity.com/paper/2512.00995/) (2025)
- [XDen-1K: A Density Field Dataset of Real-World Objects](https://mlsanity.com/paper/2512.10668/) (2025)
- [PARSE: Part-Aware Relational Spatial Modeling](https://mlsanity.com/paper/2603.07704/) (2026)
- [Lookalike3D: Seeing Double in 3D](https://mlsanity.com/paper/2603.24713/) (2026)

## Abstract (arXiv)

Segmenting 3D assets into their constituent parts is crucial for enhancing 3D understanding, facilitating model reuse, and supporting various applications such as part generation. However, current methods face limitations such as poor robustness when dealing with complex objects and cannot fully automate the process. In this paper, we propose a native 3D point-promptable part segmentation model termed P3-SAM, designed to fully automate the segmentation of any 3D objects into components. Inspired by SAM, P3-SAM consists of a feature extractor, multiple segmentation heads, and an IoU predictor, enabling interactive segmentation for users. We also propose an algorithm to automatically select and merge masks predicted by our model for part instance segmentation. Our model is trained on a newly built dataset containing nearly 3.7 million models with reasonable segmentation labels. Comparisons show that our method achieves precise segmentation results and strong robustness on any complex objects, attaining state-of-the-art performance. Our project page is available at https://murcherful.github.io/P3-SAM/.