# PartNet: A Recursive Part Decomposition Network for Fine-grained and Hierarchical Shape Segmentation

arXiv: https://arxiv.org/abs/1903.00709 · HTML: https://mlsanity.com/paper/1903.00709/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2019.

## Built on by (most cited first, 30 of 44)

- [GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable Parts](https://mlsanity.com/paper/2211.05272/) (2022)
- [PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models](https://mlsanity.com/paper/2212.01558/) (2022)
- [3D AffordanceNet: A Benchmark for Visual Object Affordance Understanding](https://mlsanity.com/paper/2103.16397/) (2021)
- [PARTFIELD: Learning 3D Feature Fields for Part Segmentation and Beyond](https://mlsanity.com/paper/2504.11451/) (2025)
- [Unsupervised Learning for Cuboid Shape Abstraction via Joint Segmentation from Point Clouds](https://mlsanity.com/paper/2106.03437/) (2021)
- [SceneHGN: Hierarchical Graph Networks for 3D Indoor Scene Generation with Fine-Grained Geometry](https://mlsanity.com/paper/2302.10237/) (2023)
- [PartSLIP++: Enhancing Low-Shot 3D Part Segmentation via Multi-View Instance Segmentation and Maximum Likelihood Estimation](https://mlsanity.com/paper/2312.03015/) (2023)
- [BuildingNet: Learning to Label 3D Buildings](https://mlsanity.com/paper/2110.04955/) (2021)
- [3D Highlighter: Localizing Regions on 3D Shapes via Text Descriptions](https://mlsanity.com/paper/2212.11263/) (2022)
- [Deep Learning Based 3D Segmentation: A Survey](https://mlsanity.com/paper/2103.05423/) (2021)
- [VLGrammar: Grounded Grammar Induction of Vision and Language](https://mlsanity.com/paper/2103.12975/) (2021)
- [RIM-Net: Recursive Implicit Fields for Unsupervised Learning of Hierarchical Shape Structures](https://mlsanity.com/paper/2201.12763/) (2022)
- [ZeroPS: High-quality Cross-modal Knowledge Transfer for Zero-Shot 3D Part Segmentation](https://mlsanity.com/paper/2311.14262/) (2023)
- [LayoutDETR: Detection Transformer Is a Good Multimodal Layout Designer](https://mlsanity.com/paper/2212.09877/) (2022)
- [Learning Fine-Grained Segmentation of 3D Shapes without Part Labels](https://mlsanity.com/paper/2103.13030/) (2021)
- [3D-QueryIS: A Query-based Framework for 3D Instance Segmentation](https://mlsanity.com/paper/2211.09375/) (2022)
- [Rearrangement Planning for General Part Assembly](https://mlsanity.com/paper/2307.00206/) (2023)
- [Style2Fab: Functionality-Aware Segmentation for Fabricating Personalized 3D Models with Generative AI](https://mlsanity.com/paper/2309.06379/) (2023)
- [MVP-SEG: Multi-View Prompt Learning for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2304.06957/) (2023)
- [Self-supervised Learning of Hybrid Part-aware 3D Representations of 2D Gaussians and Superquadrics](https://mlsanity.com/paper/2408.10789/) (2024)
- [Task-Aware Sampling Layer for Point-Wise Analysis](https://mlsanity.com/paper/2107.04291/) (2021)
- [PlaneRecTR++: Unified Query Learning for Joint 3D Planar Reconstruction and Pose Estimation](https://mlsanity.com/paper/2307.13756/) (2023)
- [Active Learning of Neural Collision Handler for Complex 3D Mesh Deformations](https://mlsanity.com/paper/2110.07727/) (2021)
- [SPIN: Hierarchical Segmentation with Subpart Granularity in Natural Images](https://mlsanity.com/paper/2407.09686/) (2024)
- [3D Shape Knowledge Graph for Cross-domain 3D Shape Retrieval](https://mlsanity.com/paper/2210.15136/) (2022)
- [Explain What You See: Open-Ended Segmentation and Recognition of Occluded 3D Objects](https://mlsanity.com/paper/2301.07037/) (2023)
- [HAL3D: Hierarchical Active Learning for Fine-Grained 3D Part Labeling](https://mlsanity.com/paper/2301.10460/) (2023)
- [Learning to Infer Parameterized Representations of Plants from 3D Scans](https://mlsanity.com/paper/2505.22337/) (2025)
- [ReGroup: Recursive Neural Networks for Hierarchical Grouping of Vector Graphic Primitives](https://mlsanity.com/paper/2111.11759/) (2021)
- [Semantic Segmentation-Assisted Instance Feature Fusion for Multi-Level 3D Part Instance Segmentation](https://mlsanity.com/paper/2208.04766/) (2022)

## Abstract (arXiv)

Deep learning approaches to 3D shape segmentation are typically formulated as a multi-class labeling problem. Existing models are trained for a fixed set of labels, which greatly limits their flexibility and adaptivity. We opt for top-down recursive decomposition and develop the first deep learning model for hierarchical segmentation of 3D shapes, based on recursive neural networks. Starting from a full shape represented as a point cloud, our model performs recursive binary decomposition, where the decomposition network at all nodes in the hierarchy share weights. At each node, a node classifier is trained to determine the type (adjacency or symmetry) and stopping criteria of its decomposition. The features extracted in higher level nodes are recursively propagated to lower level ones. Thus, the meaningful decompositions in higher levels provide strong contextual cues constraining the segmentations in lower levels. Meanwhile, to increase the segmentation accuracy at each node, we enhance the recursive contextual feature with the shape feature extracted for the corresponding part. Our method segments a 3D shape in point cloud into an unfixed number of parts, depending on the shape complexity, showing strong generality and flexibility. It achieves the state-of-the-art performance, both for fine-grained and semantic segmentation, on the public benchmark and a new benchmark of fine-grained segmentation proposed in this work. We also demonstrate its application for fine-grained part refinements in image-to-shape reconstruction.