LamiGauss: Pitching Radiative Gaussian for Sparse-View X-ray Laminography Reconstruction

• URL: http://arxiv.org/abs/2509.13863v1
• Date: Wed, 17 Sep 2025 09:53:47 GMT
• Title: LamiGauss: Pitching Radiative Gaussian for Sparse-View X-ray Laminography Reconstruction
• Abstract summary: X-ray Computed Laminography (CL) is essential for non-destructive inspection of plate-like structures in microchips and composite battery materials.<n>We propose a reconstruction algorithm, namely LamiGauss, that combines Gaussian Splatting radiativeization with a dedicated detector-to-world transformation model.<n>Our approach effectively optimize directly from sparse projections, enabling accurate and efficient reconstruction with limited data.
• Score: 27.301249008852768
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: X-ray Computed Laminography (CL) is essential for non-destructive inspection of plate-like structures in applications such as microchips and composite battery materials, where traditional computed tomography (CT) struggles due to geometric constraints. However, reconstructing high-quality volumes from laminographic projections remains challenging, particularly under highly sparse-view acquisition conditions. In this paper, we propose a reconstruction algorithm, namely LamiGauss, that combines Gaussian Splatting radiative rasterization with a dedicated detector-to-world transformation model incorporating the laminographic tilt angle. LamiGauss leverages an initialization strategy that explicitly filters out common laminographic artifacts from the preliminary reconstruction, preventing redundant Gaussians from being allocated to false structures and thereby concentrating model capacity on representing the genuine object. Our approach effectively optimizes directly from sparse projections, enabling accurate and efficient reconstruction with limited data. Extensive experiments on both synthetic and real datasets demonstrate the effectiveness and superiority of the proposed method over existing techniques. LamiGauss uses only 3$\%$ of full views to achieve superior performance over the iterative method optimized on a full dataset.

Related papers

• Projection-Volume Fidelity Divergence: Diagnosing and Controlling Optimization Drift in Sparse-View 3D Gaussian Tomography [11.284905409634957]
  We propose LADES, a ground-truth-free optimization controller for sparse-view Gaussian tomography.<n>Experiments on sparse-view CT reconstruction show that LADES improves volumetric fidelity, suppresses structural degeneration, and substantially reduces training time.
  arXiv  Detail & Related papers  (2026-06-21T14:24:02Z)
• Active View Selection with Perturbed Gaussian Ensemble for Tomographic Reconstruction [26.60542816157164]
  Sparse-view computed tomography (CT) is critical for reducing radiation exposure to patients.<n>Recent advances in radiative 3D Gaussian Splatting have enabled fast and accurate sparse-view CT reconstruction.<n>Existing active view selection methods fail to capture the unique geometric ambiguities and physical attenuation properties inherent in Xray imaging.
  arXiv  Detail & Related papers  (2026-03-06T20:16:00Z)
• TG-Field: Geometry-Aware Radiative Gaussian Fields for Tomographic Reconstruction [16.246538335191982]
  Tomographic Geometry Field (TG-Field) is a geometry-aware Gaussian deformation framework for computed tomography (CT) reconstruction.<n> TG-Field consistently outperforms existing methods, achieving state-of-the-art reconstruction accuracy under highly sparse-view conditions.
  arXiv  Detail & Related papers  (2026-02-12T08:33:01Z)
• Monte Carlo Maximum Likelihood Reconstruction for Digital Holography with Speckle [18.93229414605695]
  In coherent imaging, speckle is statistically modeled as multiplicative noise, posing a fundamental challenge for image reconstruction.<n>We propose a randomized linear algebra approach that enables scalable MLE optimization without explicit matrix inversions in gradient computation.<n>Our code is available at: https://github.com/Computational-Imaging-RU/MC_Maximum_Likelihood_Digital_Holography_Speckle.
  arXiv  Detail & Related papers  (2026-02-10T22:31:22Z)
• Radioactive 3D Gaussian Ray Tracing for Tomographic Reconstruction [10.822437634828303]
  R2-Gaussian extended the 3DGS paradigm to tomographic reconstruction.<n>We propose a tomographic reconstruction framework based on 3D Gaussian ray tracing.
  arXiv  Detail & Related papers  (2026-02-01T06:53:42Z)
• Intern-GS: Vision Model Guided Sparse-View 3D Gaussian Splatting [95.61137026932062]
  Intern-GS is a novel approach to enhance the process of sparse-view Gaussian splatting.<n>We show that Intern-GS achieves state-of-the-art rendering quality across diverse datasets.
  arXiv  Detail & Related papers  (2025-05-27T05:17:49Z)
• ProtoGS: Efficient and High-Quality Rendering with 3D Gaussian Prototypes [81.48624894781257]
  3D Gaussian Splatting (3DGS) has made significant strides in novel view synthesis but is limited by the substantial number of Gaussian primitives required.<n>Recent methods address this issue by compressing the storage size of densified Gaussians, yet fail to preserve rendering quality and efficiency.<n>We propose ProtoGS to learn Gaussian prototypes to represent Gaussian primitives, significantly reducing the total Gaussian amount without sacrificing visual quality.
  arXiv  Detail & Related papers  (2025-03-21T18:55:14Z)
• MonoGSDF: Exploring Monocular Geometric Cues for Gaussian Splatting-Guided Implicit Surface Reconstruction [86.87464903285208]
  We introduce MonoGSDF, a novel method that couples primitives with a neural Signed Distance Field (SDF) for high-quality reconstruction.<n>To handle arbitrary-scale scenes, we propose a scaling strategy for robust generalization.<n>Experiments on real-world datasets outperforms prior methods while maintaining efficiency.
  arXiv  Detail & Related papers  (2024-11-25T20:07:07Z)
• Discretized Gaussian Representation for Tomographic Reconstruction [20.390232991700977]
  We propose a novel Discretized Gaussian Representation (DGR) for Computed Tomography (CT) reconstruction.<n>DGR directly reconstructs the 3D volume using a set of discretized Gaussian functions in an end-to-end manner.<n>Our experiments on both real-world and synthetic datasets demonstrate that DGR achieves superior reconstruction quality and significantly improved computational efficiency.
  arXiv  Detail & Related papers  (2024-11-07T16:32:29Z)
• GeoSplatting: Towards Geometry Guided Gaussian Splatting for Physically-based Inverse Rendering [69.67264955234494]
  GeoSplatting is a novel approach that augments 3DGS with explicit geometry guidance for precise light transport modeling.<n>By differentiably constructing a surface-grounded 3DGS from an optimizable mesh, our approach leverages well-defined mesh normals and the opaque mesh surface.<n>This enhancement ensures precise material decomposition while preserving the efficiency and high-quality rendering capabilities of 3DGS.
  arXiv  Detail & Related papers  (2024-10-31T17:57:07Z)
• R$^2$-Gaussian: Rectifying Radiative Gaussian Splatting for Tomographic Reconstruction [53.19869886963333]
  3D Gaussian splatting (3DGS) has shown promising results in rendering image and surface reconstruction. This paper introduces R2$-Gaussian, the first 3DGS-based framework for sparse-view tomographic reconstruction.
  arXiv  Detail & Related papers  (2024-05-31T08:39:02Z)
• Geometric Constraints Enable Self-Supervised Sinogram Inpainting in Sparse-View Tomography [7.416898042520079]
  Sparse-angle tomographic scans reduce radiation and accelerate data acquisition, but suffer from image artifacts and noise. Existing image processing algorithms can restore CT reconstruction quality but often require large training data sets or can not be used for truncated objects. This work presents a self-supervised projection inpainting method that allows optimizing missing projective views via gradient-based optimization.
  arXiv  Detail & Related papers  (2023-02-13T15:15:18Z)
• Multi-Channel Convolutional Analysis Operator Learning for Dual-Energy CT Reconstruction [108.06731611196291]
  We develop a multi-channel convolutional analysis operator learning (MCAOL) method to exploit common spatial features within attenuation images at different energies. We propose an optimization method which jointly reconstructs the attenuation images at low and high energies with a mixed norm regularization on the sparse features.
  arXiv  Detail & Related papers  (2022-03-10T14:22:54Z)
• Generalizable Limited-Angle CT Reconstruction via Sinogram Extrapolation [17.084877038511557]
  We propose ExtraPolationNetwork for limited-angle CT reconstruction via the introduction of a sinogram extrapolation module. Our reconstruction model achieves state-of-the-art performance on NIH-AAPM dataset, similar to existing approaches.
  arXiv  Detail & Related papers  (2021-03-09T06:58:09Z)

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.