ACCURATE: Arbitrary-shaped Continuum Reconstruction Under Robust Adaptive Two-view Estimation

• URL: http://arxiv.org/abs/2603.07533v1
• Date: Sun, 08 Mar 2026 08:44:41 GMT
• Title: ACCURATE: Arbitrary-shaped Continuum Reconstruction Under Robust Adaptive Two-view Estimation
• Abstract summary: ACCURATE is a 3D reconstruction framework integrating an image segmentation neural network with a geometry-constrained topology and dynamic programming algorithm.<n>Our method achieves high reconstruction accuracy on both simulated and real phantom datasets, with mean absolute errors below 1.0 mm.
• Score: 3.344730946122235
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate reconstruction of arbitrary-shaped long slender continuum bodies, such as guidewires, catheters and other soft continuum manipulators, is essential for accurate mechanical simulation. However, existing image-based reconstruction approaches often suffer from limited accuracy because they often underutilize camera geometry, or lack generality as they rely on rigid geometric assumptions that may fail for continuum robots with complex and highly deformable shapes. To address these limitations, we propose ACCURATE, a 3D reconstruction framework integrating an image segmentation neural network with a geometry-constrained topology traversal and dynamic programming algorithm that enforces global biplanar geometric consistency, minimizes the cumulative point-to-epipolar-line distance, and remains robust to occlusions and epipolar ambiguities cases caused by noise and discretization. Our method achieves high reconstruction accuracy on both simulated and real phantom datasets acquired using a clinical X-ray C-arm system, with mean absolute errors below 1.0 mm.

Related papers

• Beyond Isotropic Assumptions: Continuity-Constrained Segmentation and GPU Morphometry for Nanoscale GBM Analysis [1.3107669223114085]
  Confocal microscopy of optically cleared and swelled tissue resolves complex biological structures in 3D.<n>Training a segmentation model requires dense annotations in the upsampled space, a prohibitive labeling burden.<n>We present an end-to-end, GPU-accelerated framework that overcomes this without additional annotations.
  arXiv  Detail & Related papers  (2026-08-04T18:28:19Z)
• Reliability-Aware 3D Geometric Injection for Universal Person Re-identification [47.874819213762926]
  We propose a Universal Monocular 3D-Enhanced ReID framework driven by a Consistency-Aware Reliability Gate and Dual-Stream Residual Fusion.<n>Our method improves challenging, structure-sensitive scenarios while preserving competitive performance on clean domains.
  arXiv  Detail & Related papers  (2026-07-21T08:54:25Z)
• NoDrift3R: Raymap-Guided Coupling for Drift-Robust Unposed Feed-Forward 3D Reconstruction [25.145305440192875]
  Pose-Free Feed-forward 3D Gaussian Splatting has recently emerged as a powerful paradigm for fast scene reconstruction.<n>However, its performance degrades significantly in long image sequences due to cumulative camera pose estimation drift.<n>We propose a synergistic pose-free framework that explicitly couples geometry and appearance via a Raymap-Guided Coupling Module.
  arXiv  Detail & Related papers  (2026-07-08T09:02:18Z)
• Personalized 4D Whole-Heart Mesh Reconstruction from Cine MRI via Multi-Scale Temporal Modeling and Differentiable Contour Rendering [7.111748961723236]
  We propose a novel end-to-end framework for reconstructing temporally resolved whole-heart meshes from 2D cine MRI sequences.<n>The framework incorporates a differentiable contour inspired by the Beer-Lambert attenuation principle, enabling anatomy-aware supervision of 3D+t mesh deformation.<n>The proposed method achieved a whole-heart mean absolute error of 1.68 $pm$ 0.31 mm and a motion jitter of 0.77 $pm$ 0.17 $mathrmmm/mathrmframe3$, outperforming existing methods with lower reconstruction error and substantially improved motion smoothness.
  arXiv  Detail & Related papers  (2026-07-02T09:42:46Z)
• Reliev3R: Relieving Feed-forward Reconstruction from Multi-View Geometric Annotations [98.66466590444553]
  We propose Reliev3R, a weakly-supervised paradigm for training FFRMs from scratch without cost-prohibitive multi-view geometric annotations.<n>At the core of Reliev3R, we design an ambiguity-aware relative depth loss and a trigonometry-based reprojection loss to facilitate supervision for multi-view geometric consistency.
  arXiv  Detail & Related papers  (2026-04-01T06:46:54Z)
• ERGO: Excess-Risk-Guided Optimization for High-Fidelity Monocular 3D Gaussian Splatting [63.138778159026934]
  We propose an adaptive optimization framework guided by excess risk decomposition, termed ERGO.<n> ERGO dynamically estimates the view-specific excess risk and adaptively adjust loss weights during optimization.<n>Experiments on the Google Scanned Objects dataset and the OmniObject3D dataset demonstrate the superiority of ERGO over existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-02-10T20:44:43Z)
• Advanced Geometric Correction Algorithms for 3D Medical Reconstruction: Comparison of Computed Tomography and Macroscopic Imaging [0.9395222766576343]
  This paper introduces a hybrid two-stage registration framework for reconstructing 3D kidney anatomy from macroscopic slices.<n>It addresses the data-scarcity and high-distortion challenges typical of macroscopic imaging.<n>The proposed framework generalizes to other soft-tissue organs reconstructed from optical or photographic cross-sections.
  arXiv  Detail & Related papers  (2026-01-30T17:16:17Z)
• Spinal Line Detection for Posture Evaluation through Train-ing-free 3D Human Body Reconstruction with 2D Depth Images [1.655840696749311]
  It is important to restore the 3D shape of the human body and estimate the spine center line.<n>Existing mul-ti-image-based body restoration methods require expensive equipment and complex pro-cedures.<n>This study proposes a method to compensate for the shortcomings of the multi-image-based method and to solve the limitations of the sin-gle-image method.
  arXiv  Detail & Related papers  (2025-12-14T14:43:42Z)
• EMGauss: Continuous Slice-to-3D Reconstruction via Dynamic Gaussian Modeling in Volume Electron Microscopy [41.838228673736076]
  We present EMGauss, a general framework for 3D reconstruction from planar scanned 2D slices with applications in Volume electron microscopy.<n>Our key innovation is to reframe slice-to-3D reconstruction as a 3D dynamic scene rendering problem based on Gaussian splatting.<n>Compared with diffusion- and GAN-based reconstruction methods, EMGauss substantially improves quality, enables continuous slice synthesis, and eliminates the need for large-scale pretraining.
  arXiv  Detail & Related papers  (2025-12-07T06:39:57Z)
• Dental3R: Geometry-Aware Pairing for Intraoral 3D Reconstruction from Sparse-View Photographs [10.70146635420186]
  We propose textbfDental3R, a pose-free, graph-guided pipeline for robust, high-fidelity reconstruction from sparse intraoral photographs.<n>Our method first constructs a Geometry-Aware Pairing Strategy (GAPS) to intelligently select a compact subgraph of high-value image pairs.<n>By enforcing band-limited fidelity using a discrete wavelet transform, our approach preserves fine enamel boundaries and interproximal edges while suppressing high-frequency artifacts.
  arXiv  Detail & Related papers  (2025-11-18T10:20:22Z)
• Accelerating 3D Photoacoustic Computed Tomography with End-to-End Physics-Aware Neural Operators [74.65171736966131]
  Photoacoustic computed tomography (PACT) combines optical contrast with ultrasonic resolution, achieving deep-tissue imaging beyond the optical diffusion limit.<n>Current implementations require dense transducer arrays and prolonged acquisition times, limiting clinical translation.<n>We introduce Pano, an end-to-end physics-aware model that directly learns the inverse acoustic mapping from sensor measurements to volumetric reconstructions.
  arXiv  Detail & Related papers  (2025-09-11T23:12:55Z)
• X$^{2}$-Gaussian: 4D Radiative Gaussian Splatting for Continuous-time Tomographic Reconstruction [64.2059940799033]
  Current methods discretize temporal resolution into fixed phases with respiratory gating devices.<n>X$2$-Gaussian, a novel framework, enables continuous-time 4DCT reconstruction by integrating dynamic radiative splatting with self-supervised respiratory motion learning.
  arXiv  Detail & Related papers  (2025-03-27T17:59:57Z)
• Neural Computed Tomography [1.7188280334580197]
  Motion during acquisition of a set of projections can lead to significant motion artifacts in computed tomography reconstructions. We propose a novel reconstruction framework, NeuralCT, to generate time-resolved images free from motion artifacts.
  arXiv  Detail & Related papers  (2022-01-17T18:50:58Z)
• 3D Reconstruction of Curvilinear Structures with Stereo Matching DeepConvolutional Neural Networks [52.710012864395246]
  We propose a fully automated pipeline for both detection and matching of curvilinear structures in stereo pairs. We mainly focus on 3D reconstruction of dislocations from stereo pairs of TEM images.
  arXiv  Detail & Related papers  (2021-10-14T23:05:47Z)
• Limited-angle tomographic reconstruction of dense layered objects by dynamical machine learning [68.9515120904028]
  Limited-angle tomography of strongly scattering quasi-transparent objects is a challenging, highly ill-posed problem. Regularizing priors are necessary to reduce artifacts by improving the condition of such problems. We devised a recurrent neural network (RNN) architecture with a novel split-convolutional gated recurrent unit (SC-GRU) as the building block.
  arXiv  Detail & Related papers  (2020-07-21T11:48:22Z)
• Dense Non-Rigid Structure from Motion: A Manifold Viewpoint [162.88686222340962]
  Non-Rigid Structure-from-Motion (NRSfM) problem aims to recover 3D geometry of a deforming object from its 2D feature correspondences across multiple frames. We show that our approach significantly improves accuracy, scalability, and robustness against noise.
  arXiv  Detail & Related papers  (2020-06-15T09:15:54Z)

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.