A Geometry-Driven, Framework-Agnostic Optimization for Object Pose Estimation

• URL: http://arxiv.org/abs/2608.26859v1
• Date: Thu, 27 Aug 2026 09:26:23 GMT
• Title: A Geometry-Driven, Framework-Agnostic Optimization for Object Pose Estimation
• Abstract summary: This paper introduces a data-centric optimization by proposing a novel, physically grounded rotation representation.<n>Our method aligns the object's coordinate system with its inherent geometric axes, derived from inertial properties.<n>We validate the framework across diverse category-level and instance-level models.
• Score: 14.2683286590081
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current object pose estimation research remains predominantly model-centric, focusing on architectural innovations and post-processing refinements. This paper introduces a data-centric optimization by proposing a novel, physically grounded rotation representation through principal axes alignment. Our method aligns the object's coordinate system with its inherent geometric axes, derived from inertial properties, yielding three key advantages: Inherent Stability-leveraging the energy-minimizing property of principal axes provides a robust representation that is less sensitive to noise and occlusions; Symmetry-Aware Canonicalization-explicitly resolving rotational ambiguities for symmetric objects at the data level, which fundamentally eliminates label confusion during network training; and Framework Agnosticism-the optimization is applied purely at the dataset level, ensuring plug-and-play compatibility with existing networks without any architectural modification. We validate the framework across diverse category-level and instance-level models. Extensive experiments demonstrate consistent and significant accuracy improvements, while preserving the integrity of the baseline network. This work establishes a new, geometry-driven direction for enhancing pose estimation, circumventing the need for complex network redesign.

Related papers

• RODR: Riemannian Orthogonally Decoupled Regularization for Disentangled Manifold Representation [0.91963810502252]
  We introduce a generic and interpretable framework for disentangled geometric optimization in point cloud processing.<n> Experiments demonstrate that RODR reaches performance comparable to state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-07-27T03:12:14Z)
• TriCons-Pose: Triangle-Invariant Geometric Consistency Learning for Category-Level Object Pose Estimation [22.314486838497746]
  Category-level object pose estimation is a crucial yet challenging task in both academia and industry.<n>We develop a novel Triangle-Invariant Geometric Consistency Learning for Category-Level Object Pose Estimation (TriCons-Pose) to anchor stable keypoints and aggregate pose-invariant cues.
  arXiv  Detail & Related papers  (2026-07-12T13:05:12Z)
• Beyond Point-Wise Matching: Structural Representation Alignment for Accelerating Diffusion Transformers [93.3976834364707]
  We propose sREPA, a structural REPresentation Alignment framework to enforce consistency in the relational geometry of feature maps.<n>By encouraging the model to internalize holistic spatial layouts and structural correlations from pre-trained features, sREPA achieves faster and more stable convergence.
  arXiv  Detail & Related papers  (2026-05-16T12:01:04Z)
• Towards Foundation Models for 3D Scene Understanding: Instance-Aware Self-Supervised Learning for Point Clouds [53.82500407523346]
  PointINS is an instance-oriented self-supervised framework that enriches point cloud representations through geometry-aware learning.<n>PointINS achieves on average +3.5% mAP improvement for indoor instance segmentation and +4.1% PQ gain for outdoor panoptic segmentation.
  arXiv  Detail & Related papers  (2026-03-26T08:31:06Z)
• IoUCert: Robustness Verification for Anchor-based Object Detectors [58.35703549470485]
  We introduce IoUCert, a novel formal verification framework designed specifically to overcome these bottlenecks in anchor-based object detection architectures.<n>We show that our method enables the robustness verification of realistic, anchor-based models including SSD, YOLOv2, and YOLOv3 variants against various input perturbations.
  arXiv  Detail & Related papers  (2026-03-03T14:36:46Z)
• Follow My Hold: Hand-Object Interaction Reconstruction through Geometric Guidance [61.41904916189093]
  We propose a novel diffusion-based framework for reconstructing 3D geometry of hand-held objects from monocular RGB images.<n>We use hand-object interaction as geometric guidance to ensure plausible hand-object interactions.
  arXiv  Detail & Related papers  (2025-08-25T17:11:53Z)
• PAID: Pairwise Angular-Invariant Decomposition for Continual Test-Time Adaptation [70.98107766265636]
  This paper takes the geometric attributes of pre-trained weights as a starting point, systematically analyzing three key components: magnitude, absolute angle, and pairwise angular structure.<n>We find that the pairwise angular structure remains stable across diverse corrupted domains and encodes domain-invariant semantic information, suggesting it should be preserved during adaptation.
  arXiv  Detail & Related papers  (2025-06-03T05:18:15Z)
• Geometry-Editable and Appearance-Preserving Object Compositon [67.98806888489385]
  General object composition (GOC) aims to seamlessly integrate a target object into a background scene with desired geometric properties.<n>Recent approaches derive semantic embeddings and integrate them into advanced diffusion models to enable geometry-editable generation.<n>We introduce a Disentangled Geometry-editable and Appearance-preserving Diffusion model that first leverages semantic embeddings to implicitly capture desired geometric transformations.
  arXiv  Detail & Related papers  (2025-05-27T09:05:28Z)
• OCMG-Net: Neural Oriented Normal Refinement for Unstructured Point Clouds [18.234146052486054]
  We present a robust refinement method for estimating oriented normals from unstructured point clouds. Our framework incorporates sign orientation and data augmentation in the feature space to refine the initial oriented normals. To address the issue of noise-caused direction inconsistency existing in previous approaches, we introduce a new metric called the Chamfer Normal Distance.
  arXiv  Detail & Related papers  (2024-09-02T09:30:02Z)
• Leveraging Equivariant Features for Absolute Pose Regression [9.30597356471664]
  We show that a translation and rotation equivariant Convolutional Neural Network directly induces representations of camera motions into the feature space. We then show that this geometric property allows for implicitly augmenting the training data under a whole group of image plane-preserving transformations.
  arXiv  Detail & Related papers  (2022-04-05T12:44:20Z)
• A Smooth Representation of Belief over SO(3) for Deep Rotation Learning with Uncertainty [33.627068152037815]
  We present a novel symmetric matrix representation of the 3D rotation group, SO(3), with two important properties that make it particularly suitable for learned models. We empirically validate the benefits of our formulation by training deep neural rotation regressors on two data modalities. This capability is key for safety-critical applications where detecting novel inputs can prevent catastrophic failure of learned models.
  arXiv  Detail & Related papers  (2020-06-01T15:57:45Z)

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.