# CorNet: Generic 3D Corners for 6D Pose Estimation of New Objects without Retraining

arXiv: https://arxiv.org/abs/1908.11457 · HTML: https://mlsanity.com/paper/1908.11457/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2019.

## Built on by (most cited first, 14 of 14)

- [Gen6D: Generalizable Model-Free 6-DoF Object Pose Estimation from RGB Images](https://mlsanity.com/paper/2204.10776/) (2022)
- [FoundPose: Unseen Object Pose Estimation with Foundation Features](https://mlsanity.com/paper/2311.18809/) (2023)
- [OSOP: A Multi-Stage One Shot Object Pose Estimation Framework](https://mlsanity.com/paper/2203.15533/) (2022)
- [Deep Learning-Based Object Pose Estimation: A Comprehensive Survey](https://mlsanity.com/paper/2405.07801/) (2024)
- [PoseContrast: Class-Agnostic Object Viewpoint Estimation in the Wild with Pose-Aware Contrastive Learning](https://mlsanity.com/paper/2105.05643/) (2021)
- [PoseMatcher: One-shot 6D Object Pose Estimation by Deep Feature Matching](https://mlsanity.com/paper/2304.01382/) (2023)
- [Scalable Unseen Objects 6-DoF Absolute Pose Estimation with Robotic Integration](https://mlsanity.com/paper/2503.05578/) (2025)
- [Extreme Two-View Geometry From Object Poses with Diffusion Models](https://mlsanity.com/paper/2402.02800/) (2024)
- [Imitrob: Imitation Learning Dataset for Training and Evaluating 6D Object Pose Estimators](https://mlsanity.com/paper/2209.07976/) (2022)
- [3D-Augmented Contrastive Knowledge Distillation for Image-based Object Pose Estimation](https://mlsanity.com/paper/2206.02531/) (2022)
- [Generalizable Single-view Object Pose Estimation by Two-side Generating and Matching](https://mlsanity.com/paper/2411.15860/) (2024)
- [SingRef6D: Monocular Novel Object Pose Estimation with a Single RGB Reference](https://mlsanity.com/paper/2509.21927/) (2025)
- [Detection and Pose Estimation of flat, Texture-less Industry Objects on HoloLens using synthetic Training](https://mlsanity.com/paper/2402.04979/) (2024)
- [HACMatch Semi-Supervised Rotation Regression with Hardness-Aware Curriculum Pseudo Labeling](https://mlsanity.com/paper/2603.21583/) (2026)

## Abstract (arXiv)

We present a novel approach to the detection and 3D pose estimation of objects in color images. Its main contribution is that it does not require any training phases nor data for new objects, while state-of-the-art methods typically require hours of training time and hundreds of training registered images. Instead, our method relies only on the objects' geometries. Our method focuses on objects with prominent corners, which covers a large number of industrial objects. We first learn to detect object corners of various shapes in images and also to predict their 3D poses, by using training images of a small set of objects. To detect a new object in a given image, we first identify its corners from its CAD model; we also detect the corners visible in the image and predict their 3D poses. We then introduce a RANSAC-like algorithm that robustly and efficiently detects and estimates the object's 3D pose by matching its corners on the CAD model with their detected counterparts in the image. Because we also estimate the 3D poses of the corners in the image, detecting only 1 or 2 corners is sufficient to estimate the pose of the object, which makes the approach robust to occlusions. We finally rely on a final check that exploits the full 3D geometry of the objects, in case multiple objects have the same corner spatial arrangement. The advantages of our approach make it particularly attractive for industrial contexts, and we demonstrate our approach on the challenging T-LESS dataset.