# OnePose++: Keypoint-Free One-Shot Object Pose Estimation without CAD Models

arXiv: https://arxiv.org/abs/2301.07673 · HTML: https://mlsanity.com/paper/2301.07673/

## Key facts

- Cited by 115 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 67 references that are in the map.
- First posted on arXiv 18 Jan 2023.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention](https://mlsanity.com/paper/2006.16236/) (2020)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [LoFTR: Detector-Free Local Feature Matching with Transformers](https://mlsanity.com/paper/2104.00680/) (2021)
- [PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes](https://mlsanity.com/paper/1711.00199/) (2017)
- [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017)
- [MegaDepth: Learning Single-View Depth Prediction from Internet Photos](https://mlsanity.com/paper/1804.00607/) (2018)
- [From Coarse to Fine: Robust Hierarchical Localization at Large Scale](https://mlsanity.com/paper/1812.03506/) (2018)
- [Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation](https://mlsanity.com/paper/1901.02970/) (2019)
- [DISK: Learning local features with policy gradient](https://mlsanity.com/paper/2006.13566/) (2020)
- [SSD-6D: Making RGB-based 3D detection and 6D pose estimation great again](https://mlsanity.com/paper/1711.10006/) (2017)
- [Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation](https://mlsanity.com/paper/1908.07433/) (2019)
- [Pixel-Perfect Structure-from-Motion with Featuremetric Refinement](https://mlsanity.com/paper/2108.08291/) (2021)
- [DPOD: 6D Pose Object Detector and Refiner](https://mlsanity.com/paper/1902.11020/) (2019)
- [Shape Prior Deformation for Categorical 6D Object Pose and Size Estimation](https://mlsanity.com/paper/2007.08454/) (2020)
- [OnePose: One-Shot Object Pose Estimation without CAD Models](https://mlsanity.com/paper/2205.12257/) (2022)
- [Patch2Pix: Epipolar-Guided Pixel-Level Correspondences](https://mlsanity.com/paper/2012.01909/) (2020)
- [Gen6D: Generalizable Model-Free 6-DoF Object Pose Estimation from RGB Images](https://mlsanity.com/paper/2204.10776/) (2022)
- [Learning Feature Descriptors using Camera Pose Supervision](https://mlsanity.com/paper/2004.13324/) (2020)
- [Category Level Object Pose Estimation via Neural Analysis-by-Synthesis](https://mlsanity.com/paper/2008.08145/) (2020)
- [Dual-Resolution Correspondence Networks](https://mlsanity.com/paper/2006.08844/) (2020)
- [LatentFusion: End-to-End Differentiable Reconstruction and Rendering for Unseen Object Pose Estimation](https://mlsanity.com/paper/1912.00416/) (2019)
- [OSOP: A Multi-Stage One Shot Object Pose Estimation Framework](https://mlsanity.com/paper/2203.15533/) (2022)
- [Category-Level 6D Object Pose Estimation via Cascaded Relation and Recurrent Reconstruction Networks](https://mlsanity.com/paper/2108.08755/) (2021)
- [NeRF-Pose: A First-Reconstruct-Then-Regress Approach for Weakly-supervised 6D Object Pose Estimation](https://mlsanity.com/paper/2203.04802/) (2022)
- [Category-Level Metric Scale Object Shape and Pose Estimation](https://mlsanity.com/paper/2109.00326/) (2021)
- [Multi-View Optimization of Local Feature Geometry](https://mlsanity.com/paper/2003.08348/) (2020)

## Built on by (most cited first, 30 of 115)

- [FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects](https://mlsanity.com/paper/2312.08344/) (2023)
- [SAM-6D: Segment Anything Model Meets Zero-Shot 6D Object Pose Estimation](https://mlsanity.com/paper/2311.15707/) (2023)
- [FoundPose: Unseen Object Pose Estimation with Foundation Features](https://mlsanity.com/paper/2311.18809/) (2023)
- [Detector-Free Structure from Motion](https://mlsanity.com/paper/2306.15669/) (2023)
- [FreeZe: Training-free zero-shot 6D pose estimation with geometric and vision foundation models](https://mlsanity.com/paper/2312.00947/) (2023)
- [Any6D: Model-free 6D Pose Estimation of Novel Objects](https://mlsanity.com/paper/2503.18673/) (2025)
- [Deep Learning-Based Object Pose Estimation: A Comprehensive Survey](https://mlsanity.com/paper/2405.07801/) (2024)
- [NOPE: Novel Object Pose Estimation from a Single Image](https://mlsanity.com/paper/2303.13612/) (2023)
- [Robotic Manipulation by Imitating Generated Videos Without Physical Demonstrations](https://mlsanity.com/paper/2507.00990/) (2025)
- [GenFlow: Generalizable Recurrent Flow for 6D Pose Refinement of Novel Objects](https://mlsanity.com/paper/2403.11510/) (2024)
- [POPE: 6-DoF Promptable Pose Estimation of Any Object, in Any Scene, with One Reference](https://mlsanity.com/paper/2305.15727/) (2023)
- [Open-vocabulary object 6D pose estimation](https://mlsanity.com/paper/2312.00690/) (2023)
- [MatchU: Matching Unseen Objects for 6D Pose Estimation from RGB-D Images](https://mlsanity.com/paper/2403.01517/) (2024)
- [6DGS: 6D Pose Estimation from a Single Image and a 3D Gaussian Splatting Model](https://mlsanity.com/paper/2407.15484/) (2024)
- [Orient Anything V2: Unifying Orientation and Rotation Understanding](https://mlsanity.com/paper/2601.05573/) (2026)
- [ZeroPose: CAD-Prompted Zero-shot Object 6D Pose Estimation in Cluttered Scenes](https://mlsanity.com/paper/2305.17934/) (2023)
- [Learning to Estimate 6DoF Pose from Limited Data: A Few-Shot, Generalizable Approach using RGB Images](https://mlsanity.com/paper/2306.07598/) (2023)
- [One2Any: One-Reference 6D Pose Estimation for Any Object](https://mlsanity.com/paper/2505.04109/) (2025)
- [GS-Pose: Generalizable Segmentation-based 6D Object Pose Estimation with 3D Gaussian Splatting](https://mlsanity.com/paper/2403.10683/) (2024)
- [Dense-SfM: Structure from Motion with Dense Consistent Matching](https://mlsanity.com/paper/2501.14277/) (2025)
- [Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning](https://mlsanity.com/paper/2411.19458/) (2024)
- [Co-op: Correspondence-based Novel Object Pose Estimation](https://mlsanity.com/paper/2503.17731/) (2025)
- [Long-term Visual Localization with Mobile Sensors](https://mlsanity.com/paper/2304.07691/) (2023)
- [AutoRecon: Automated 3D Object Discovery and Reconstruction](https://mlsanity.com/paper/2305.08810/) (2023)
- [iComMa: Inverting 3D Gaussian Splatting for Camera Pose Estimation via Comparing and Matching](https://mlsanity.com/paper/2312.09031/) (2023)
- [Accurate and efficient zero-shot 6D pose estimation with frozen foundation models](https://mlsanity.com/paper/2506.09784/) (2025)
- [PoseMatcher: One-shot 6D Object Pose Estimation by Deep Feature Matching](https://mlsanity.com/paper/2304.01382/) (2023)
- [GS-Pose: Category-Level Object Pose Estimation via Geometric and Semantic Correspondence](https://mlsanity.com/paper/2311.13777/) (2023)
- [UNOPose: Unseen Object Pose Estimation with an Unposed RGB-D Reference Image](https://mlsanity.com/paper/2411.16106/) (2024)
- [Diff-DOPE: Differentiable Deep Object Pose Estimation](https://mlsanity.com/paper/2310.00463/) (2023)

## Abstract (arXiv)

We propose a new method for object pose estimation without CAD models. The previous feature-matching-based method OnePose has shown promising results under a one-shot setting which eliminates the need for CAD models or object-specific training. However, OnePose relies on detecting repeatable image keypoints and is thus prone to failure on low-textured objects. We propose a keypoint-free pose estimation pipeline to remove the need for repeatable keypoint detection. Built upon the detector-free feature matching method LoFTR, we devise a new keypoint-free SfM method to reconstruct a semi-dense point-cloud model for the object. Given a query image for object pose estimation, a 2D-3D matching network directly establishes 2D-3D correspondences between the query image and the reconstructed point-cloud model without first detecting keypoints in the image. Experiments show that the proposed pipeline outperforms existing one-shot CAD-model-free methods by a large margin and is comparable to CAD-model-based methods on LINEMOD even for low-textured objects. We also collect a new dataset composed of 80 sequences of 40 low-textured objects to facilitate future research on one-shot object pose estimation. The supplementary material, code and dataset are available on the project page: https://zju3dv.github.io/onepose_plus_plus/.