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arXiv · 2610.23237

X-Splat: Unified Gaussian Splatting for Joint RGB and X-ray 3D Reconstruction

Wangze Xu·Chu Zhou·Mae Yamaguchi·Linrui Dai·I-Chao Shen·Takashi Ijiri·Imari Sato
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Abstract

Jointly reconstructing RGB and X-ray provides new insights by connecting external appearance with internal structure, enabling a more complete understanding of real-world objects than single modality. Yet existing methods train separate models per modality and suffer from redundant geometry, alignment artifacts, and loose coupling that limits cross-modal insight discovery. We propose a unified representation in which all Gaussian primitives share a single set of geometric parameters, with only surface-proximal primitives additionally carrying appearance attributes. We perform cross-modal registration through clean multi-view stereo and X-ray geometric proxies instead of aligning two sets of optimized Gaussians, improving alignment stability. We then jointly optimize the unified representation via supervision from both modalities, complemented by a surface-depth consistency loss derived from X-ray geometry to guide RGB reconstruction. Experiments on the InsideOut dataset and our Flower & Insect dataset show that our framework achieves more robust cross-modal alignment, higher RGB rendering quality without compromising X-ray reconstruction, and a more compact representation than maintaining two independent geometries.

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Wangze Xu, Chu Zhou, Mae Yamaguchi, Linrui Dai, I-Chao Shen, Takashi Ijiri, Imari Sato. 2026-10-16. X-Splat: Unified Gaussian Splatting for Joint RGB and X-ray 3D Reconstruction. https://doi.org/10.1145/3829340.3842177

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