UniPose9D: Universal Category-Agnostic Object Pose Estimation

• URL: http://arxiv.org/abs/2607.09985v1
• Date: Fri, 10 Jul 2026 21:24:23 GMT
• Title: UniPose9D: Universal Category-Agnostic Object Pose Estimation
• Abstract summary: UniPose9D is a category-agnostic foundation model for 9D object pose estimation.<n>It estimates rotation, translation, and metric size without category labels, CAD models, mean-shape priors, or reference views.<n>To improve accuracy, we introduce a point-pair-based RANSAC N-hop Kabsch--Umeyama algorithm with an adaptive threshold.
• Score: 10.964744555206243
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Object pose estimation is a fundamental problem in 3D vision. Although recent state-of-the-art approaches achieve strong performance, they often overfit to existing benchmarks and exhibit limited generalization to novel categories and unseen scenes. We propose UniPose9D, a category-agnostic foundation model for 9D object pose estimation: given an instance mask/ROI and either an RGB-D observation or an RGB image with predicted depth, the model estimates rotation, translation, and metric size without category labels, CAD models, mean-shape priors, or reference views. Specifically, UniPose9D samples point pairs from the observed object geometry and uses DINOv2 and PointNet features to predict NOCS coordinates for each pair. To improve accuracy, we introduce a point-pair-based RANSAC N-hop Kabsch--Umeyama algorithm with an adaptive threshold. We further employ flow matching to address symmetric ambiguities and construct a large-scale training set by curating and aligning pose annotations from existing public datasets. Experiments across six datasets show that a single unified model can match or surpass specialist methods while generalizing to unseen objects and in-the-wild scenarios. Our code and model are available on https://github.com/qq456cvb/UniPose9D.

Related papers

• Object Pose Transformer: Unifying Unseen Object Pose Estimation [54.20344997573707]
  Learning model-free object pose estimation for unseen instances remains a fundamental challenge in 3D vision.<n>ours jointly predicts depth, point maps, camera parameters, and normalized object coordinates from RGB inputs.<n>ours is camera-agnostic, learning camera intrinsics on-the-fly and supporting optional depth input for metric-scale recovery.
  arXiv  Detail & Related papers  (2026-03-24T16:04:16Z)
• Beyond 'Templates': Category-Agnostic Object Pose, Size, and Shape Estimation from a Single View [69.6117755984012]
  Estimating an object's 6D pose, size, and shape from visual input is a fundamental problem in computer vision.<n>We propose a unified category-agnostic framework that simultaneously predicts 6D pose, size, and dense shape from a single RGB-D image.
  arXiv  Detail & Related papers  (2025-10-13T17:49:15Z)
• UNOPose: Unseen Object Pose Estimation with an Unposed RGB-D Reference Image [86.7128543480229]
  Unseen object pose estimation methods often rely on CAD models or multiple reference views.<n>To simplify reference acquisition, we aim to estimate the unseen object's pose through a single unposed RGB-D reference image.<n>We present a novel approach and benchmark, termed UNOPose, for unseen one-reference-based object pose estimation.
  arXiv  Detail & Related papers  (2024-11-25T05:36:00Z)
• OV9D: Open-Vocabulary Category-Level 9D Object Pose and Size Estimation [56.028185293563325]
  This paper studies a new open-set problem, the open-vocabulary category-level object pose and size estimation. We first introduce OO3D-9D, a large-scale photorealistic dataset for this task. We then propose a framework built on pre-trained DinoV2 and text-to-image stable diffusion models.
  arXiv  Detail & Related papers  (2024-03-19T03:09:24Z)
• FoundPose: Unseen Object Pose Estimation with Foundation Features [11.32559845631345]
  FoundPose is a model-based method for 6D pose estimation of unseen objects from a single RGB image. The method can quickly onboard new objects using their 3D models without requiring any object- or task-specific training.
  arXiv  Detail & Related papers  (2023-11-30T18:52:29Z)
• Generative Category-Level Shape and Pose Estimation with Semantic Primitives [27.692997522812615]
  We propose a novel framework for category-level object shape and pose estimation from a single RGB-D image. To handle the intra-category variation, we adopt a semantic primitive representation that encodes diverse shapes into a unified latent space. We show that the proposed method achieves SOTA pose estimation performance and better generalization in the real-world dataset.
  arXiv  Detail & Related papers  (2022-10-03T17:51:54Z)
• ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation [76.31125154523056]
  We present a discrete descriptor, which can represent the object surface densely. We also propose a coarse to fine training strategy, which enables fine-grained correspondence prediction.
  arXiv  Detail & Related papers  (2022-03-17T16:16:24Z)
• GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting [103.74918834553249]
  GPV-Pose is a novel framework for robust category-level pose estimation. It harnesses geometric insights to enhance the learning of category-level pose-sensitive features. It produces superior results to state-of-the-art competitors on common public benchmarks.
  arXiv  Detail & Related papers  (2022-03-15T13:58:50Z)
• Single-stage Keypoint-based Category-level Object Pose Estimation from an RGB Image [27.234658117816103]
  We propose a single-stage, keypoint-based approach for category-level object pose estimation. The proposed network performs 2D object detection, detects 2D keypoints, estimates 6-DoF pose, and regresses relative bounding cuboid dimensions. We conduct extensive experiments on the challenging Objectron benchmark, outperforming state-of-the-art methods on the 3D IoU metric.
  arXiv  Detail & Related papers  (2021-09-13T17:55:00Z)
• 6D Object Pose Estimation using Keypoints and Part Affinity Fields [24.126513851779936]
  The task of 6D object pose estimation from RGB images is an important requirement for autonomous service robots to be able to interact with the real world. We present a two-step pipeline for estimating the 6 DoF translation and orientation of known objects.
  arXiv  Detail & Related papers  (2021-07-05T14:41:19Z)
• Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training [3.8073142980733]
  We propose a novel framework for monocular 3D objects detection using only RGB images, called KM3D-Net. We design a fully convolutional model to predict object keypoints, dimension, and orientation, and then combine these estimations with perspective geometry constraints to compute position attribute.
  arXiv  Detail & Related papers  (2020-09-02T00:51:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.