# Revisiting Pose Sensitivity in Splat-based Computed Tomography under Sparse-view Reconstruction

arXiv: https://arxiv.org/abs/2608.04752 · HTML: https://mlsanity.com/paper/2608.04752/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 53 references that are in the map.
- First posted on arXiv 5 Aug 2026.

## Builds on (most cited first, 15 of 15)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [A micro Lie theory for state estimation in robotics](https://mlsanity.com/paper/1812.01537/) (2018)
- [NAF: Neural Attenuation Fields for Sparse-View CBCT Reconstruction](https://mlsanity.com/paper/2209.14540/) (2022)
- [Structure-Aware Sparse-View X-ray 3D Reconstruction](https://mlsanity.com/paper/2311.10959/) (2023)
- [R2-Gaussian: Rectifying Radiative Gaussian Splatting for Tomographic Reconstruction](https://mlsanity.com/paper/2405.20693/) (2024)
- [Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis](https://mlsanity.com/paper/2403.04116/) (2024)
- [GS-CPR: Efficient Camera Pose Refinement via 3D Gaussian Splatting](https://mlsanity.com/paper/2408.11085/) (2024)
- [Sparse-view Cone Beam CT Reconstruction using Data-consistent Supervised and Adversarial Learning from Scarce Training Data](https://mlsanity.com/paper/2201.09318/) (2022)
- [Self-Calibrating Gaussian Splatting for Large Field of View Reconstruction](https://mlsanity.com/paper/2502.09563/) (2025)
- [Sparse-view Pose Estimation and Reconstruction via Analysis by Generative Synthesis](https://mlsanity.com/paper/2412.03570/) (2024)
- [Gradient-Based Geometry Learning for Fan-Beam CT Reconstruction](https://mlsanity.com/paper/2212.02177/) (2022)
- [Joint Rigid Motion Correction and Sparse-View CT via Self-Calibrating Neural Field](https://mlsanity.com/paper/2210.12731/) (2022)
- [A gradient-based approach to fast and accurate head motion compensation in cone-beam CT](https://mlsanity.com/paper/2401.09283/) (2024)
- [Deep learning-based conditional inpainting for restoration of artifact-affected 4D CT images](https://mlsanity.com/paper/2203.06431/) (2022)
- [Differentiable Score-Based Likelihoods: Learning CT Motion Compensation From Clean Images](https://mlsanity.com/paper/2404.14747/) (2024)

## Abstract (arXiv)

X-ray computed tomography (CT) reconstructs volumetric representations of objects from projection images obtained by transmitting X-rays through a target. Recent splat-based tomography, which represents a volume as a continuous distribution of 3D Gaussians, has demonstrated both high reconstruction quality and fast convergence in cone-beam sparse-view CT. However, when deployed in real CT systems with limited and non-uniform view distributions, we observe distinctive streak and strip artifacts that are far more pronounced than in conventional reconstruction methods. Through detailed analysis, we show that these artifacts primarily originate from pose inaccuracies in the acquisition geometry rather than from view sparsity itself. We revisit pose sensitivity in the splatting formulation and derive a stable gradient-based framework that jointly refines geometric parameters during reconstruction. Our study not only identifies how pose perturbations propagate through the differentiable projection operator but also reveals why splat-based CT is particularly vulnerable to geometric misalignment. The resulting formulation remains lightweight and easily integrable into existing pipelines while substantially improving reconstruction fidelity under real-world sparse-view conditions.