# Unsupervised Learning of Intrinsic Structural Representation Points

arXiv: https://arxiv.org/abs/2003.01661 · HTML: https://mlsanity.com/paper/2003.01661/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2020.

## Built on by (most cited first, 30 of 39)

- [CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP](https://mlsanity.com/paper/2301.04926/) (2023)
- [UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebase](https://mlsanity.com/paper/2309.05573/) (2023)
- [Towards Label-free Scene Understanding by Vision Foundation Models](https://mlsanity.com/paper/2306.03899/) (2023)
- [KeypointDeformer: Unsupervised 3D Keypoint Discovery for Shape Control](https://mlsanity.com/paper/2104.11224/) (2021)
- [Autoregressive 3D Shape Generation via Canonical Mapping](https://mlsanity.com/paper/2204.01955/) (2022)
- [Learning 3D Dense Correspondence via Canonical Point Autoencoder](https://mlsanity.com/paper/2107.04867/) (2021)
- [Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive Learning](https://mlsanity.com/paper/2111.10827/) (2021)
- [Point2SSM: Learning Morphological Variations of Anatomies from Point Cloud](https://mlsanity.com/paper/2305.14486/) (2023)
- [SOCS: Semantically-aware Object Coordinate Space for Category-Level 6D Object Pose Estimation under Large Shape Variations](https://mlsanity.com/paper/2303.10346/) (2023)
- [Semi-supervised Anatomical Landmark Detection via Shape-regulated Self-training](https://mlsanity.com/paper/2105.13593/) (2021)
- [3x2: 3D Object Part Segmentation by 2D Semantic Correspondences](https://mlsanity.com/paper/2407.09648/) (2024)
- [DV-Matcher: Deformation-based Non-Rigid Point Cloud Matching Guided by Pre-trained Visual Features](https://mlsanity.com/paper/2408.08568/) (2024)
- [One Sketch for All: One-Shot Personalized Sketch Segmentation](https://mlsanity.com/paper/2112.10838/) (2021)
- [Zero-shot point cloud segmentation by transferring geometric primitives](https://mlsanity.com/paper/2210.09923/) (2022)
- [SC3K: Self-supervised and Coherent 3D Keypoints Estimation from Rotated, Noisy, and Decimated Point Cloud Data](https://mlsanity.com/paper/2308.05410/) (2023)
- [Structure-Aware 3D VR Sketch to 3D Shape Retrieval](https://mlsanity.com/paper/2209.09043/) (2022)
- [Towards 3D Scene Understanding by Referring Synthetic Models](https://mlsanity.com/paper/2203.10546/) (2022)
- [Unsupervised Learning of 3D Semantic Keypoints with Mutual Reconstruction](https://mlsanity.com/paper/2203.10212/) (2022)
- [Deep Deformable Models: Learning 3D Shape Abstractions with Part Consistency](https://mlsanity.com/paper/2309.01035/) (2023)
- [Point2SSM++: Self-Supervised Learning of Anatomical Shape Models from Point Clouds](https://mlsanity.com/paper/2405.09707/) (2024)
- [Key-Grid: Unsupervised 3D Keypoints Detection using Grid Heatmap Features](https://mlsanity.com/paper/2410.02237/) (2024)
- [RPG: Learning Recursive Point Cloud Generation](https://mlsanity.com/paper/2105.14322/) (2021)
- [SnapshotNet: Self-supervised Feature Learning for Point Cloud Data Segmentation Using Minimal Labeled Data](https://mlsanity.com/paper/2201.04833/) (2022)
- [Domain Generalization for Mammographic Image Analysis with Contrastive Learning](https://mlsanity.com/paper/2304.10226/) (2023)
- [Model2Scene: Learning 3D Scene Representation via Contrastive Language-CAD Models Pre-training](https://mlsanity.com/paper/2309.16956/) (2023)
- [Towards Learning Geometric Eigen-Lengths Crucial for Fitting Tasks](https://mlsanity.com/paper/2312.15610/) (2023)
- [Unsupervised 3D Keypoint Discovery with Multi-View Geometry](https://mlsanity.com/paper/2211.12829/) (2022)
- [Learning Implicit Functions for Dense 3D Shape Correspondence of Generic Objects](https://mlsanity.com/paper/2212.14276/) (2022)
- [3D Registration in 30 Years: A Survey](https://mlsanity.com/paper/2412.13735/) (2024)
- [Mesh2SSM++: A Probabilistic Framework for Unsupervised Learning of Statistical Shape Model of Anatomies from Surface Meshes](https://mlsanity.com/paper/2502.07145/) (2025)

## Abstract (arXiv)

Learning structures of 3D shapes is a fundamental problem in the field of computer graphics and geometry processing. We present a simple yet interpretable unsupervised method for learning a new structural representation in the form of 3D structure points. The 3D structure points produced by our method encode the shape structure intrinsically and exhibit semantic consistency across all the shape instances with similar structures. This is a challenging goal that has not fully been achieved by other methods. Specifically, our method takes a 3D point cloud as input and encodes it as a set of local features. The local features are then passed through a novel point integration module to produce a set of 3D structure points. The chamfer distance is used as reconstruction loss to ensure the structure points lie close to the input point cloud. Extensive experiments have shown that our method outperforms the state-of-the-art on the semantic shape correspondence task and achieves comparable performance with the state-of-the-art on the segmentation label transfer task. Moreover, the PCA based shape embedding built upon consistent structure points demonstrates good performance in preserving the shape structures. Code is available at https://github.com/NolenChen/3DStructurePoints