XClipGS: Exact Half-Space Clipping for Medical Volume Gaussian Splatting

2026-08-07

AI Summary

This work addresses the challenge of accurately clipping Gaussian splats against arbitrary planes in medical volume rendering, where conventional methods often suffer from structural distortion or information leakage due to imprecise intersection handling. The authors propose a differentiable, closed-form clipping operator that requires no additional parameters, deriving an analytical solution for half-space constrained Gaussian integrals based on a local affine model. By integrating conditional Gaussian cumulative distribution functions within the EWA rendering framework, the method achieves precise clipping. Evaluated on eight CT and MRI datasets using novel metrics—Clipping Distortion Error (CDE) and Leakage (Leak)—alongside multi-distance and multi-angle supervision, the approach attains a PSNR of 33.56 dB (outperforming ClipGS), a cross-sectional SSIM of 0.860, and approximately 40× lower leakage. It consistently achieves state-of-the-art results across all four metrics under arbitrary clipping normals while maintaining a rendering speed exceeding 650 FPS.

Abstract

Gaussian-splatting proxies enable interactive rendering of volumetric medical scans, but a clipping plane exposes anatomy not constrained by external-view training and intersects primitives that conventional splatting can only keep or drop whole. We present XClipGS (eXact Clipping), which treats these as two separate problems: the render-time clip operator and supervision of the hidden interior. Under the local affine model used by EWA splatting, the ray integral of a half-space-restricted Gaussian factorizes exactly into its ordinary 2D footprint and a conditional Gaussian CDF whose argument is affine in pixel coordinates. The resulting closed-form per-pixel operator introduces no learned clipping parameters or auxiliary network and remains differentiable with respect to the primitive and plane. We use multi-distance reference views with varied clipping-plane axes and offsets to supervise the interior through the same operator. We also introduce a paired clipped/unclipped cut-face protocol with difference-referenced cut error (CDE) and culled-side leakage (Leak), because global image metrics dilute errors near the plane. On eight CT and MRI volumes with plane offsets not used for training, XClipGS attains the highest PSNR on every volume (33.56 versus 32.34 dB for ClipGS) while rendering at over 650 FPS, far above real time, versus 278 FPS. On voxel-axis cut-face views, it raises average band SSIM from 0.809 to 0.860 and leaks roughly 40 times less. Without retraining, it also achieves the best average across all four metrics on arbitrary-normal planes; on a fixed interior, it matches RaRa's face fidelity with about 16 times less leakage. Project page: https://gaozhongpai.github.io/XClipGS/

Problem

• Gaussian Splatting
• Medical Volume Rendering
• Half-Space Clipping
• Clipping Plane
• Interactive Visualization

Innovation

• Gaussian Splatting
• Exact Clipping
• Half-Space Clipping
• Differentiable Rendering
• Medical Volume Visualization