Residual Gaussian Splatting for Ultra Sparse-View CBCT Reconstruction

• URL: http://arxiv.org/abs/2604.27552v1
• Date: Thu, 30 Apr 2026 07:59:18 GMT
• Title: Residual Gaussian Splatting for Ultra Sparse-View CBCT Reconstruction
• Abstract summary: This work integrates wavelet multi-resolution analysis with 3DGS to enhance sparse reconstruction.<n>We introduce a spectrally-decoupled Gaussian representation that stratifies the volumetric field into a geometric base component and a residual detail component.<n>Experiments on clinical datasets demonstrate that RGS enables the reconstructed images to capture highly refined geometric textures.
• Score: 14.00351191217297
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While 3D Gaussian splatting (3DGS) offers explicit and efficient scene representations for cone-beam computed tomography reconstruction, conventional photometric optimization inherently suffers from spectral bias under ultra sparse-view conditions, leading to over-smoothing and a loss of high-frequency anatomical details. Since wavelet transforms provide rich high-frequency information and have been widely utilized to enhance sparse reconstruction, this work integrates wavelet multi-resolution analysis with 3DGS. To circumvent the mathematical mismatch between the strict non-negativity of physical X-ray attenuation and the bipolar nature of high-frequency wavelet coefficients, we propose Residual Gaussian Splatting (RGS). Methodologically, we introduce a spectrally-decoupled Gaussian representation that stratifies the volumetric field into a geometric base component and a residual detail component. This decomposition systematically transforms explicit high-frequency fitting into a physically consistent, implicit residual compensation task. Furthermore, we devise a spectral-spatial collaborative optimization strategy to coordinate the interplay between geometric anchoring and texture refinement, effectively preventing spectral crosstalk. Extensive experiments on clinical datasets demonstrate that RGS enables the reconstructed images to capture highly refined geometric textures. It successfully resolves the trade-off between artifact suppression and detail preservation, yielding superior visual fidelity in complex trabecular and vascular structures compared to existing neural rendering baselines.

Related papers

• Revisiting Pose Sensitivity in Splat-based Computed Tomography under Sparse-view Reconstruction [12.315488379543773]
  Recent splat-based tomography has demonstrated both high reconstruction quality and fast convergence in cone-beam sparse-view CT.<n>We observe distinctive streak and strip artifacts that are far more pronounced than in conventional reconstruction methods.<n>We derive a stable gradient-based framework that jointly refines geometric parameters during reconstruction.
  arXiv  Detail & Related papers  (2026-08-05T12:20:53Z)
• Multivariate Gaussian NeRF for Wide Field-of-View Ultrasound Reconstruction [34.01678594740569]
  We introduce Ultra-Wide-NeRF, a 3D Gaussian (MVG) NeRF-based method for WFoV ultrasound reconstruction.<n>Our method inherently mitigates these compounding artifacts and provides anti-aliasing.<n>We validate Ultra-Wide-NeRF for intracardiac echocardiography on phantom and porcine datasets.
  arXiv  Detail & Related papers  (2026-04-27T08:48:49Z)
• MSGS: Multispectral 3D Gaussian Splatting [46.61006851229471]
  We present a multispectral extension to 3D Gaussian Splatting (3DGS) for wavelength-aware view synthesis.<n>Each Gaussian is augmented with spectral radiance, represented via per-band spherical harmonics, and optimized under a dual-loss supervision scheme combining RGB and multispectral signals.<n>Our method is evaluated on both public and self-captured real-world datasets, demonstrating consistent improvements over the RGB-only 3DGS baseline in terms of image quality and spectral consistency.
  arXiv  Detail & Related papers  (2026-04-14T23:03:38Z)
• PolGS++: Physically-Guided Polarimetric Gaussian Splatting for Fast Reflective Surface Reconstruction [76.9760302941819]
  PolGS++ is a polarimetric Gaussian Splatting framework for fast reflective surface reconstruction.<n>We introduce a depth-guided visibility mask acquisition mechanism that enables angle-of-polarization (AoP)-based tangent-space consistency constraints.
  arXiv  Detail & Related papers  (2026-03-11T14:11:42Z)
• 3DGS-HPC: Distractor-free 3D Gaussian Splatting with Hybrid Patch-wise Classification [80.43401018217367]
  3D Gaussian Splatting (3DGS) has demonstrated remarkable performance in novel view synthesis and 3D scene reconstruction.<n>Its quality often degrades in real-world environments due to transient distractors, such as moving objects and varying shadows.<n>Existing methods rely on semantic cues extracted from pre-trained vision models to identify and suppress these distractors.<n>We propose 3DGS- HPC, a framework that circumvents these limitations by combining two complementary principles.
  arXiv  Detail & Related papers  (2026-03-08T11:16:30Z)
• 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)
• FSP-Diff: Full-Spectrum Prior-Enhanced DualDomain Latent Diffusion for Ultra-Low-Dose Spectral CT Reconstruction [20.226435074884808]
  FSP-Diff is a full-spectrum prior-enhanced dual-domain latent diffusion framework for ultra-low-dose spectral CT reconstruction.<n>Our framework integrates direct image reconstructions with projection-domain denoised results.<n>Experiments on simulated and real-world datasets demonstrate that FSP-Diff significantly outperforms state-of-the-art methods in both image quality and computational efficiency.
  arXiv  Detail & Related papers  (2026-02-08T14:13:19Z)
• Towards Integrating Multi-Spectral Imaging with Gaussian Splatting [8.908394706712395]
  We present a study of how to integrate color (RGB) and multi-spectral imagery into the 3D Gaussian Splatting framework.<n>We suggest integrating multi-spectral data directly into the spherical harmonics color components to compactly model each Gaussian's multi-spectral reflectance.
  arXiv  Detail & Related papers  (2025-08-31T20:53:35Z)
• Diffusion Denoised Hyperspectral Gaussian Splatting [11.486860334986394]
  3D reconstruction methods have been used to create implicit neural representations of hyperspectral scenes.<n>We propose Diffusion-Denoised Hyperspectral Gaussian Splatting (DD-HGS) to enable 3D explicit reconstruction of hyperspectral scenes.
  arXiv  Detail & Related papers  (2025-05-28T02:07:52Z)
• 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)
• Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenes [50.92217884840301]
  Gaussian Opacity Fields (GOF) is a novel approach for efficient, high-quality, and adaptive surface reconstruction in scenes. GOF is derived from ray-tracing-based volume rendering of 3D Gaussians. GOF surpasses existing 3DGS-based methods in surface reconstruction and novel view synthesis.
  arXiv  Detail & Related papers  (2024-04-16T17:57:19Z)

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.