# UKPGAN: A General Self-Supervised Keypoint Detector

arXiv: https://arxiv.org/abs/2011.11974 · HTML: https://mlsanity.com/paper/2011.11974/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2020.

## Built on by (most cited first, 15 of 15)

- [USEEK: Unsupervised SE(3)-Equivariant 3D Keypoints for Generalizable Manipulation](https://mlsanity.com/paper/2209.13864/) (2022)
- [3D Implicit Transporter for Temporally Consistent Keypoint Discovery](https://mlsanity.com/paper/2309.05098/) (2023)
- [NCP: Neural Correspondence Prior for Effective Unsupervised Shape Matching](https://mlsanity.com/paper/2301.05839/) (2023)
- [Back to 3D: Few-Shot 3D Keypoint Detection with Back-Projected 2D Features](https://mlsanity.com/paper/2311.18113/) (2023)
- [SNAKE: Shape-aware Neural 3D Keypoint Field](https://mlsanity.com/paper/2206.01724/) (2022)
- [3D Human Keypoints Estimation From Point Clouds in the Wild Without Human Labels](https://mlsanity.com/paper/2306.04745/) (2023)
- [SC3K: Self-supervised and Coherent 3D Keypoints Estimation from Rotated, Noisy, and Decimated Point Cloud Data](https://mlsanity.com/paper/2308.05410/) (2023)
- [PatchAlign3D: Local Feature Alignment for Dense 3D Shape Understanding](https://mlsanity.com/paper/2601.02457/) (2026)
- [Key-Grid: Unsupervised 3D Keypoints Detection using Grid Heatmap Features](https://mlsanity.com/paper/2410.02237/) (2024)
- [Deep Learning-Based Object Detection in Maritime Unmanned Aerial Vehicle Imagery: Review and Experimental Comparisons](https://mlsanity.com/paper/2311.07955/) (2023)
- [SelfGeo: Self-supervised and Geodesic-consistent Estimation of Keypoints on Deformable Shapes](https://mlsanity.com/paper/2408.02291/) (2024)
- [Exploring Category-level Articulated Object Pose Tracking on SE(3) Manifolds](https://mlsanity.com/paper/2511.05996/) (2025)
- [KeyPointDiffuser: Unsupervised 3D Keypoint Learning via Latent Diffusion Models](https://mlsanity.com/paper/2512.03450/) (2025)
- [Learned Suppression for 3D Keypoint Detection with a Graph-Transformer Backbone](https://mlsanity.com/paper/2605.15088/) (2026)
- [Feed-Forward Hierarchical Gaussian Diffusion for Extreme CT Reconstruction](https://mlsanity.com/paper/2608.15343/) (2026)

## Abstract (arXiv)

Keypoint detection is an essential component for the object registration and alignment. In this work, we reckon keypoint detection as information compression, and force the model to distill out irrelevant points of an object. Based on this, we propose UKPGAN, a general self-supervised 3D keypoint detector where keypoints are detected so that they could reconstruct the original object shape. Two modules: GAN-based keypoint sparsity control and salient information distillation modules are proposed to locate those important keypoints. Extensive experiments show that our keypoints align well with human annotated keypoint labels, and can be applied to SMPL human bodies under various non-rigid deformations. Furthermore, our keypoint detector trained on clean object collections generalizes well to real-world scenarios, thus further improves geometric registration when combined with off-the-shelf point descriptors. Repeatability experiments show that our model is stable under both rigid and non-rigid transformations, with local reference frame estimation. Our code is available on https://github.com/qq456cvb/UKPGAN.