统一适配各类相机的3D高斯点渲染框架，解决畸变导致的重建不一致问题。
UniTriSplat: A Unified 3D Gaussian Splatting Framework with Uniform Spherical Rasterization for Universal Cameras
- 在单位球面上用HEALPix网格统一采样，实现跨相机模型的均匀渲染
- 支持从广角到全景图像的统一优化，重建质量提升显著
- 适合多类型相机数据融合与全景重建场景
现有3D高斯点渲染（3DGS）框架依赖特定相机的光栅化方式，导致异构相机模型（如透视、鱼眼、全向）间存在不一致的立体角采样，性能下降。为此，我们提出UniTriSplat，一种适用于通用相机的统一3DGS框架，通过HEALPix离散化在单位球面上重构高斯点渲染。利用HEALPix的等面积特性，构建与输入图像角分辨率对齐的球面采样网格，并直接在球面弧度域推导高斯点的前向渲染与梯度传播，实现从窄视场图像到360度全景图的一致优化行为。为提升感知重建质量，进一步引入考虑球面邻域结构的HEALPix感知SSIM损失。在多种相机模型上的大量实验表明，UniTriSplat在保持几何保真度和渲染质量的同时，显著提升了跨相机泛化能力。
原文摘要 · Abstract (English)
Existing 3D Gaussian Splatting (3DGS) frameworks rely on camera-specific rasterization, suffering from inconsistent solid-angle sampling and degraded performance across heterogeneous camera models (e.g., perspective, fisheye, omnidirectional). To address this limitation, we propose UniTriSplat, a unified 3DGS framework for universal cameras that reformulates Gaussian splatting on the unit sphere via HEALPix discretization. Leveraging the equal-area property of HEALPix, we construct a spherical sampling grid aligned with the angular resolution of input images. We derive the forward rendering and gradient propagation of Gaussians directly in the spherical radian domain, yielding uniform optimization behavior from narrow-FoV images to full 360-degree panoramas. To enhance perceptual reconstruction quality, we additionally introduce a HEALPix-aware SSIM loss that respects spherical neighborhood structure. Extensive experiments across diverse camera models demonstrate that UniTriSplat consistently improves cross-camera generalization while preserving geometric fidelity and rendering quality.
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