Generalizable Single-view Object Pose Estimation by Two-side Generating and Matching

• URL: http://arxiv.org/abs/2411.15860v1
• Date: Sun, 24 Nov 2024 14:31:50 GMT
• Title: Generalizable Single-view Object Pose Estimation by Two-side Generating and Matching
• Authors: Yujing Sun, Caiyi Sun, Yuan Liu, Yuexin Ma, Siu Ming Yiu,
• Abstract summary: 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.
• Score: 19.730504197461144
• License:
• Abstract: In this paper, we present a novel generalizable object pose estimation method to determine the object pose using only one RGB image. Unlike traditional approaches that rely on instance-level object pose estimation and necessitate extensive training data, 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. These characteristics are achieved by utilizing a diffusion model to generate novel-view images and conducting a two-sided matching on these generated images. Quantitative experiments demonstrate the superiority of our method over existing pose estimation techniques across both synthetic and real-world datasets. Remarkably, our approach maintains strong performance even in scenarios with significant viewpoint changes, highlighting its robustness and versatility in challenging conditions. The code will be re leased at https://github.com/scy639/Gen2SM.

Related papers

• Extreme Two-View Geometry From Object Poses with Diffusion Models [21.16779160086591]
  We harness the power of object priors to accurately determine two-view geometry in the face of extreme viewpoint changes. In experiments, our method has demonstrated extraordinary robustness and resilience to large viewpoint changes.
  arXiv  Detail & Related papers  (2024-02-05T08:18:47Z)
• 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)
• GS-Pose: Category-Level Object Pose Estimation via Geometric and Semantic Correspondence [5.500735640045456]
  Category-level pose estimation is a challenging task with many potential applications in computer vision and robotics. We propose to utilize both geometric and semantic features obtained from a pre-trained foundation model. This requires significantly less data to train than prior methods since the semantic features are robust to object texture and appearance.
  arXiv  Detail & Related papers  (2023-11-23T02:35:38Z)
• 3D-Aware Hypothesis & Verification for Generalizable Relative Object Pose Estimation [69.73691477825079]
  We present a new hypothesis-and-verification framework to tackle the problem of generalizable object pose estimation. To measure reliability, we introduce a 3D-aware verification that explicitly applies 3D transformations to the 3D object representations learned from the two input images.
  arXiv  Detail & Related papers  (2023-10-05T13:34:07Z)
• MegaPose: 6D Pose Estimation of Novel Objects via Render & Compare [84.80956484848505]
  MegaPose is a method to estimate the 6D pose of novel objects, that is, objects unseen during training. We present a 6D pose refiner based on a render&compare strategy which can be applied to novel objects. Second, we introduce a novel approach for coarse pose estimation which leverages a network trained to classify whether the pose error between a synthetic rendering and an observed image of the same object can be corrected by the refiner.
  arXiv  Detail & Related papers  (2022-12-13T19:30:03Z)
• 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)
• Fusing Local Similarities for Retrieval-based 3D Orientation Estimation of Unseen Objects [70.49392581592089]
  We tackle the task of estimating the 3D orientation of previously-unseen objects from monocular images. We follow a retrieval-based strategy and prevent the network from learning object-specific features. Our experiments on the LineMOD, LineMOD-Occluded, and T-LESS datasets show that our method yields a significantly better generalization to unseen objects than previous works.
  arXiv  Detail & Related papers  (2022-03-16T08:53:00Z)
• Disentangled Implicit Shape and Pose Learning for Scalable 6D Pose Estimation [44.8872454995923]
  We present a novel approach for scalable 6D pose estimation, by self-supervised learning on synthetic data of multiple objects using a single autoencoder. We test our method on two multi-object benchmarks with real data, T-LESS and NOCS REAL275, and show it outperforms existing RGB-based methods in terms of pose estimation accuracy and generalization.
  arXiv  Detail & Related papers  (2021-07-27T01:55:30Z)
• Single View Metrology in the Wild [94.7005246862618]
  We present a novel approach to single view metrology that can recover the absolute scale of a scene represented by 3D heights of objects or camera height above the ground. Our method relies on data-driven priors learned by a deep network specifically designed to imbibe weakly supervised constraints from the interplay of the unknown camera with 3D entities such as object heights. We demonstrate state-of-the-art qualitative and quantitative results on several datasets as well as applications including virtual object insertion.
  arXiv  Detail & Related papers  (2020-07-18T22:31:33Z)
• 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.