3D scene reconstruction is fundamental for spatial intelligence applications such as AR, robotics, and digital twins. Traditional multi-view stereo struggles with sparse viewpoints or low-texture regions, while neural rendering approaches, though capable of producing high-quality results, require per-scene optimization and lack real-time efficiency. Explicit 3D Gaussian Splatting (3DGS) enables efficient rendering, but most feed-forward variants focus on visual quality rather than geometric consistency, limiting accurate surface reconstruction and overall reliability in spatial perception tasks. This paper presents a novel feed-forward 3DGS framework for 360 images, capable of generating geometrically consistent Gaussian primitives while maintaining high rendering quality. A Depth-Normal geometric regularization is introduced to couple rendered depth gradients with normal information, supervising Gaussian rotation, scale, and position to improve point cloud and surface accuracy. Experimental results show that the proposed method maintains high rendering quality while significantly improving geometric consistency, providing an effective solution for 3D reconstruction in spatial perception tasks.

翻译：三维场景重建是增强现实、机器人学和数字孪生等空间智能应用的基础任务。传统多视图立体方法在稀疏视点或低纹理区域存在困难，而神经渲染方法虽能生成高质量结果，却需要逐场景优化且缺乏实时效率。显式三维高斯溅射（3DGS）能够实现高效渲染，但多数前馈式变体侧重于视觉质量而非几何一致性，限制了其在空间感知任务中精确表面重建与整体可靠性。本文提出一种面向360度图像的新型前馈式3DGS框架，能够在保持高渲染质量的同时生成几何一致的高斯基元。通过引入深度-法向几何正则化方法，将渲染深度梯度与法向信息耦合，对高斯旋转、尺度和位置进行联合监督，从而提升点云与表面精度。实验结果表明，所提方法在保持高渲染质量的同时显著提升了几何一致性，为空间感知任务中的三维重建提供了有效解决方案。