PoseGAM: Robust Unseen Object Pose Estimation via Geometry-Aware Multi-View Reasoning

• URL: http://arxiv.org/abs/2512.10840v1
• Date: Thu, 11 Dec 2025 17:29:25 GMT
• Title: PoseGAM: Robust Unseen Object Pose Estimation via Geometry-Aware Multi-View Reasoning
• Authors: Jianqi Chen, Biao Zhang, Xiangjun Tang, Peter Wonka,
• Abstract summary: PoseGAM is a geometry-aware multi-view framework that directly predicts object pose from a query image and multiple template images.<n>We construct a large-scale synthetic dataset containing more than 190k objects under diverse environmental conditions.
• Score: 49.66437612420291
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 6D object pose estimation, which predicts the transformation of an object relative to the camera, remains challenging for unseen objects. Existing approaches typically rely on explicitly constructing feature correspondences between the query image and either the object model or template images. In this work, we propose PoseGAM, a geometry-aware multi-view framework that directly predicts object pose from a query image and multiple template images, eliminating the need for explicit matching. Built upon recent multi-view-based foundation model architectures, the method integrates object geometry information through two complementary mechanisms: explicit point-based geometry and learned features from geometry representation networks. In addition, we construct a large-scale synthetic dataset containing more than 190k objects under diverse environmental conditions to enhance robustness and generalization. Extensive evaluations across multiple benchmarks demonstrate our state-of-the-art performance, yielding an average AR improvement of 5.1% over prior methods and achieving up to 17.6% gains on individual datasets, indicating strong generalization to unseen objects. Project page: https://windvchen.github.io/PoseGAM/ .

Related papers

• One2Any: One-Reference 6D Pose Estimation for Any Object [98.50085481362808]
  6D object pose estimation remains challenging for many applications due to dependencies on complete 3D models, multi-view images, or training limited to specific object categories.<n>We propose a novel method One2Any that estimates the relative 6-degrees of freedom (DOF) object pose using only a single reference-single query RGB-D image.<n> Experiments on multiple benchmark datasets demonstrate that our model generalizes well to novel objects, achieving state-of-the-art accuracy and even rivaling methods that require multi-view or CAD inputs, at a fraction of compute.
  arXiv  Detail & Related papers  (2025-05-07T03:54:59Z)
• Generalizable Single-view Object Pose Estimation by Two-side Generating and Matching [19.730504197461144]
  We present a novel generalizable object pose estimation method to determine the object pose using only one RGB image. Our method offers generalization to unseen objects without extensive training, operates with a single reference image of the object, and eliminates the need for 3D object models or multiple views of the object.
  arXiv  Detail & Related papers  (2024-11-24T14:31:50Z)
• EasyHOI: Unleashing the Power of Large Models for Reconstructing Hand-Object Interactions in the Wild [79.71523320368388]
  Our work aims to reconstruct hand-object interactions from a single-view image.<n>We first design a novel pipeline to estimate the underlying hand pose and object shape.<n>With the initial reconstruction, we employ a prior-guided optimization scheme.
  arXiv  Detail & Related papers  (2024-11-21T16:33:35Z)
• SINGAPO: Single Image Controlled Generation of Articulated Parts in Objects [20.978091381109294]
  We propose a method to generate articulated objects from a single image.<n>Our method generates an articulated object that is visually consistent with the input image.<n>Our experiments show that our method outperforms the state-of-the-art in articulated object creation.
  arXiv  Detail & Related papers  (2024-10-21T20:41:32Z)
• FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects [55.77542145604758]
  FoundationPose is a unified foundation model for 6D object pose estimation and tracking. Our approach can be instantly applied at test-time to a novel object without fine-tuning.
  arXiv  Detail & Related papers  (2023-12-13T18:28:09Z)
• LocPoseNet: Robust Location Prior for Unseen Object Pose Estimation [69.70498875887611]
  LocPoseNet is able to robustly learn location prior for unseen objects. Our method outperforms existing works by a large margin on LINEMOD and GenMOP.
  arXiv  Detail & Related papers  (2022-11-29T15:21:34Z)
• Learning to Complete Object Shapes for Object-level Mapping in Dynamic Scenes [30.500198859451434]
  We propose a novel object-level mapping system that can simultaneously segment, track, and reconstruct objects in dynamic scenes. It can further predict and complete their full geometries by conditioning on reconstructions from depth inputs and a category-level shape prior. We evaluate its effectiveness by quantitatively and qualitatively testing it in both synthetic and real-world sequences.
  arXiv  Detail & Related papers  (2022-08-09T22:56:33Z)
• ELLIPSDF: Joint Object Pose and Shape Optimization with a Bi-level Ellipsoid and Signed Distance Function Description [9.734266860544663]
  This paper proposes an expressive yet compact model for joint object pose and shape optimization. It infers an object-level map from multi-view RGB-D camera observations. Our approach is evaluated on the large-scale real-world ScanNet dataset and compared against state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-08-01T03:07:31Z)
• Continuous Surface Embeddings [76.86259029442624]
  We focus on the task of learning and representing dense correspondences in deformable object categories. We propose a new, learnable image-based representation of dense correspondences. We demonstrate that the proposed approach performs on par or better than the state-of-the-art methods for dense pose estimation for humans.
  arXiv  Detail & Related papers  (2020-11-24T22:52:15Z)
• Object-Centric Image Generation from Layouts [93.10217725729468]
  We develop a layout-to-image-generation method to generate complex scenes with multiple objects. Our method learns representations of the spatial relationships between objects in the scene, which lead to our model's improved layout-fidelity. We introduce SceneFID, an object-centric adaptation of the popular Fr'echet Inception Distance metric, that is better suited for multi-object images.
  arXiv  Detail & Related papers  (2020-03-16T21:40:09Z)

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.