In this paper, we propose UniGS, a unified map representation and differentiable framework for high-fidelity multimodal 3D reconstruction based on 3D Gaussian Splatting. Our framework integrates a CUDA-accelerated rasterization pipeline capable of rendering photo-realistic RGB images, geometrically accurate depth maps, consistent surface normals, and semantic logits simultaneously. We redesign the rasterization to render depth via differentiable ray-ellipsoid intersection rather than using Gaussian centers, enabling effective optimization of rotation and scale attribute through analytic depth gradients. Furthermore, we derive the analytic gradient formulation for surface normal rendering, ensuring geometric consistency among reconstructed 3D scenes. To improve computational and storage efficiency, we introduce a learnable attribute that enables differentiable pruning of Gaussians with minimal contribution during training. Quantitative and qualitative experiments demonstrate state-of-the-art reconstruction accuracy across all modalities, validating the efficacy of our geometry-aware paradigm. Source code and multimodal viewer will be available on GitHub.

翻译：本文提出UniGS，一种基于3D高斯溅射的统一地图表示与可微分框架，用于高保真多模态三维重建。该框架集成了CUDA加速的光栅化管线，能够同步渲染照片级真实感RGB图像、几何精确的深度图、一致的法线贴图以及语义逻辑值。我们重新设计了光栅化流程，通过可微分光线-椭球体求交而非高斯中心点来渲染深度，从而通过解析深度梯度实现旋转与尺度属性的有效优化。此外，我们推导了表面法线渲染的解析梯度公式，确保重建三维场景间的几何一致性。为提升计算与存储效率，我们引入可学习属性，支持在训练过程中对贡献度最小的高斯基元进行可微分剪枝。定量与定性实验表明，本方法在所有模态上均达到最先进的重建精度，验证了几何感知范式的有效性。源代码与多模态查看器将在GitHub上开源。