Better Pose Initialization for Fast and Robust 2D/3D Pelvis Registration

• URL: http://arxiv.org/abs/2503.07767v1
• Date: Mon, 10 Mar 2025 18:42:13 GMT
• Title: Better Pose Initialization for Fast and Robust 2D/3D Pelvis Registration
• Authors: Yehyun Suh, J. Ryan Martin, Daniel Moyer,
• Abstract summary: This paper presents an approach for improving 2D/3D pelvis registration in optimization-based pose estimators.<n>We find that even a coarse initializer greatly improves pose estimator accuracy, and improves overall computational efficiency.
• Score: 1.8352113484137624
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents an approach for improving 2D/3D pelvis registration in optimization-based pose estimators using a learned initialization function. Current methods often fail to converge to the optimal solution when initialized naively. We find that even a coarse initializer greatly improves pose estimator accuracy, and improves overall computational efficiency. This approach proves to be effective also in challenging cases under more extreme pose variation. Experimental validation demonstrates that our method consistently achieves robust and accurate registration, enhancing the reliability of 2D/3D registration for clinical applications.

Related papers

• Improving Instance Optimization in Deformable Image Registration with Gradient Projection [7.6061804149819885]
  Deformable image registration is inherently a multi-objective optimization problem. These conflicting objectives often lead to poor optimization outcomes. Deep learning methods have recently gained popularity in this domain due to their efficiency in processing large datasets.
  arXiv  Detail & Related papers  (2024-10-21T08:27:13Z)
• 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)
• Uncertainty-Aware Testing-Time Optimization for 3D Human Pose Estimation [68.75387874066647]
  We propose an Uncertainty-Aware testing-time optimization framework for 3D human pose estimation. Our approach outperforms the previous best result by a large margin of 4.5% on Human3.6M.
  arXiv  Detail & Related papers  (2024-02-04T04:28:02Z)
• SE(3) Diffusion Model-based Point Cloud Registration for Robust 6D Object Pose Estimation [66.16525145765604]
  We introduce an SE(3) diffusion model-based point cloud registration framework for 6D object pose estimation in real-world scenarios. Our approach formulates the 3D registration task as a denoising diffusion process, which progressively refines the pose of the source point cloud. Experiments demonstrate that our diffusion registration framework presents outstanding pose estimation performance on the real-world TUD-L, LINEMOD, and Occluded-LINEMOD datasets.
  arXiv  Detail & Related papers  (2023-10-26T12:47:26Z)
• Embedded Feature Similarity Optimization with Specific Parameter Initialization for 2D/3D Medical Image Registration [4.533408985664949]
  We present a novel deep learning-based framework for medical image registration. The proposed framework takes extracting multi-scale features into consideration using a novel composite connection with special training techniques. Our experiments demonstrate that the method in this paper has improved the registration performance, and thereby outperforms the existing methods in terms of accuracy and running time.
  arXiv  Detail & Related papers  (2023-05-10T15:33:15Z)
• Dynamic Iterative Refinement for Efficient 3D Hand Pose Estimation [87.54604263202941]
  We propose a tiny deep neural network of which partial layers are iteratively exploited for refining its previous estimations. We employ learned gating criteria to decide whether to exit from the weight-sharing loop, allowing per-sample adaptation in our model. Our method consistently outperforms state-of-the-art 2D/3D hand pose estimation approaches in terms of both accuracy and efficiency for widely used benchmarks.
  arXiv  Detail & Related papers  (2021-11-11T23:31:34Z)
• Deep Iterative 2D/3D Registration [9.813316061451392]
  We propose a novel Deep Learning driven 2D/3D registration framework that can be used end-to-end for iterative registration tasks. We accomplish this by learning the update step of the 2D/3D registration framework using Point-to-Plane Correspondences. Our proposed method achieves an average runtime of around 8s, a mean re-projection distance error of 0.60 $pm$ 0.40 mm with a success ratio of 97 percent and a capture range of 60 mm.
  arXiv  Detail & Related papers  (2021-07-21T10:51:29Z)
• Uncertainty-Aware Camera Pose Estimation from Points and Lines [101.03675842534415]
  Perspective-n-Point-and-Line (Pn$PL) aims at fast, accurate and robust camera localizations with respect to a 3D model from 2D-3D feature coordinates.
  arXiv  Detail & Related papers  (2021-07-08T15:19:36Z)
• Learning the Update Operator for 2D/3D Image Registration [10.720342813316531]
  preoperative volume can be overlaid over the 2D images using 2D/3D image registration. Deep learning-based 2D/3D registration methods have shown promising results by improving computational efficiency and robustness. We show an improvement of 1.8 times in terms of registration accuracy for the update step prediction compared to learning without the known operator.
  arXiv  Detail & Related papers  (2021-02-04T19:52:59Z)
• Canny-VO: Visual Odometry with RGB-D Cameras based on Geometric 3D-2D Edge Alignment [85.32080531133799]
  This paper reviews the classical problem of free-form curve registration and applies it to an efficient RGBD visual odometry system called Canny-VO. Two replacements for the distance transformation commonly used in edge registration are proposed: Approximate Nearest Neighbour Fields and Oriented Nearest Neighbour Fields. 3D2D edge alignment benefits from these alternative formulations in terms of both efficiency and accuracy.
  arXiv  Detail & Related papers  (2020-12-15T11:42:17Z)
• Towards Fast, Accurate and Stable 3D Dense Face Alignment [73.01620081047336]
  We propose a novel regression framework named 3DDFA-V2 which makes a balance among speed, accuracy and stability. We present a virtual synthesis method to transform one still image to a short-video which incorporates in-plane and out-of-plane face moving.
  arXiv  Detail & Related papers  (2020-09-21T15:37:37Z)
• Efficient and Robust Registration on the 3D Special Euclidean Group [11.805432720871263]
  We present an accurate, robust and fast method for registration of 3D scans.<n>We exploit the geometric properties of Lie groups as well as the robustness afforded by an iteratively reweighted least squares optimization.
  arXiv  Detail & Related papers  (2019-04-11T03:52:42Z)

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.