OMNI-PoseX: A Fast Vision Model for 6D Object Pose Estimation in Embodied Tasks

• URL: http://arxiv.org/abs/2604.02759v1
• Date: Fri, 03 Apr 2026 06:07:52 GMT
• Title: OMNI-PoseX: A Fast Vision Model for 6D Object Pose Estimation in Embodied Tasks
• Abstract summary: We present a vision foundation model that unifies open-vocabulary perception with an SO(3)-aware reflected flow matching pose predictor.<n>The architecture decouples object-level understanding from geometry-consistent rotation inference, and employs a lightweight multi-modal fusion strategy.<n>To enhance robustness and generalization, the model is trained on large-scale 6D pose datasets.
• Score: 5.49148694940665
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate 6D object pose estimation is a fundamental capability for embodied agents, yet remains highly challenging in open-world environments. Many existing methods often rely on closed-set assumptions or geometry-agnostic regression schemes, limiting their generalization, stability, and real-time applicability in robotic systems. We present OMNI-PoseX, a vision foundation model that introduces a novel network architecture unifying open-vocabulary perception with an SO(3)-aware reflected flow matching pose predictor. The architecture decouples object-level understanding from geometry-consistent rotation inference, and employs a lightweight multi-modal fusion strategy that conditions rotation-sensitive geometric features on compact semantic embeddings, enabling efficient and stable 6D pose estimation. To enhance robustness and generalization, the model is trained on large-scale 6D pose datasets, leveraging broad object diversity, viewpoint variation, and scene complexity to build a scalable open-world pose backbone. Comprehensive evaluations across benchmark pose estimation, ablation studies, zero-shot generalization, and system-level robotic grasping integration demonstrate the effectiveness of OMNI-PoseX. The OMNI-PoseX achieves SOTA pose accuracy and real-time efficiency, while delivering geometrically consistent predictions that enable reliable grasping of diverse, previously unseen objects.

Related papers

• Pose Anything Anywhere:Model-free Object Poses from Arbitrary References [64.58017857473774]
  We present PANY, a model-free framework that seamlessly supports both RGB and RGB-D inputs.<n>It operates on one or sparse pose-free reference views, and generalizes effectively to novel objects.<n>It achieves state-of-the-art performance across multiple benchmarks, substantially outperforming existing model-free methods.
  arXiv  Detail & Related papers  (2026-06-22T17:23:57Z)
• 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)
• Geo6DPose: Fast Zero-Shot 6D Object Pose Estimation via Geometry-Filtered Feature Matching [0.0]
  Geo6DPose is a lightweight, fully local, and training-free pipeline for zero-shot 6D pose estimation.<n>Geo6DPose achieves sub-second inference on a single commodity GPU while matching the average recall of significantly larger zero-shot baselines.
  arXiv  Detail & Related papers  (2025-12-11T14:20:17Z)
• OPFormer: Object Pose Estimation leveraging foundation model with geometric encoding [2.1987601456703474]
  We introduce a unified, end-to-end framework that seamlessly integrates object detection and pose estimation.<n>Our system first employs the CNOS detector to localize target objects.<n>For each detection, our novel pose estimation module, OPFormer, infers the precise 6D pose.
  arXiv  Detail & Related papers  (2025-11-16T14:19:52Z)
• UnPose: Uncertainty-Guided Diffusion Priors for Zero-Shot Pose Estimation [19.76147681894604]
  UnPose is a framework for zero-shot, model-free 6D object pose estimation and reconstruction.<n>It exploits 3D priors and uncertainty estimates from a pre-trained diffusion model.<n>It significantly outperforms existing approaches in both 6D pose estimation accuracy and 3D reconstruction quality.
  arXiv  Detail & Related papers  (2025-08-21T21:31:04Z)
• Any6D: Model-free 6D Pose Estimation of Novel Objects [76.30057578269668]
  We introduce Any6D, a model-free framework for 6D object pose estimation.<n>It requires only a single RGB-D anchor image to estimate both the 6D pose and size of unknown objects in novel scenes.<n>We evaluate our method on five challenging datasets.
  arXiv  Detail & Related papers  (2025-03-24T13:46:21Z)
• Sparse-view Pose Estimation and Reconstruction via Analysis by Generative Synthesis [25.898616784744377]
  Given a sparse set of observed views, the observations may not provide sufficient direct evidence to obtain complete and accurate 3D.<n>We propose SparseAGS, a method that adapts this analysis-by-synthesis approach by: a) including novel-view-synthesis-based generative priors in conjunction with photometric objectives to improve the quality of the inferred 3D, and b) explicitly reasoning about outliers and using a discrete search with a continuous optimization-based strategy to correct them.
  arXiv  Detail & Related papers  (2024-12-04T18:59:24Z)
• UPose3D: Uncertainty-Aware 3D Human Pose Estimation with Cross-View and Temporal Cues [55.69339788566899]
  UPose3D is a novel approach for multi-view 3D human pose estimation. It improves robustness and flexibility without requiring direct 3D annotations.
  arXiv  Detail & Related papers  (2024-04-23T00:18:00Z)
• 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)
• FS6D: Few-Shot 6D Pose Estimation of Novel Objects [116.34922994123973]
  6D object pose estimation networks are limited in their capability to scale to large numbers of object instances. In this work, we study a new open set problem; the few-shot 6D object poses estimation: estimating the 6D pose of an unknown object by a few support views without extra training.
  arXiv  Detail & Related papers  (2022-03-28T10:31:29Z)

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.