GaussianSlicer: Efficient Surface Reconstruction from Cross-sectional Slices with Gaussian Splatting

In this work, we present GaussianSlicer, an efficient plane-based Gaussian Splatting framework for reconstructing geometry from cross-sectional slices input. Unlike previous methods that rely on computationally intensive geometry-based or grid-based implicit techniques, which struggle with complex cases (e.g., sparse slices, multi-hole geometries). GaussianSlicer enables parallel optimization without requiring any prior constraints. Our method begins by initializing planar Gaussians on each slice and optimizing their layout to obtain accurate geometry representation. To align the Gaussian splats, we introduce a geometric regularization that promotes surface smoothness and ensures consistency in global topology. Our system enables accurate 3D reconstruction from sparse, irregular, multi-label slices with high computational efficiency. Experimental results show that, on average, our method is 45.62% faster and achieves a 21.91% improvement in Chamfer Distance (CD) outperforming state-of-the-art methods on the collected dataset.

I. Introduction

Slice-based cross-sectional reconstruction refers to the process of generating a 3D model from a series of cross-sectional images, or "slices" [1], [2]. These slices are typically obtained by scanning techniques such as Computed Tomography (CT) [3] – [6], Magnetic Resonance Imaging (MRI) [7] – [10], and Computer-aid Manufacturing (CAM) [11] – [13], which normally capture detailed internal views of an object at different planes.