透过反射看清场景：利用高斯泼溅推进含镜面环境中的三维场景重建
Seeing Through Reflections: Advancing 3D Scene Reconstruction in Mirror-Containing Environments with Gaussian Splatting
AI总结：
本文针对含镜面环境的三维重建难题，提出MirrorScene3D数据集和ReflectiveGS方法，利用镜面反射作为互补视角增强几何细节，在多项指标上超越现有方法。
摘要：
含镜面环境给三维重建和新视角合成（NVS）带来了独特挑战，因为反射表面会引入与视角相关的失真和不一致性。尽管神经辐射场（NeRF）和三维高斯泼溅（3DGS）等前沿方法在典型场景中表现优异，但在存在镜面的情况下其性能会下降。现有解决方案主要集中于通过对称映射处理镜面表面，但往往忽略了镜面反射所携带的丰富信息。这些反射提供了互补视角，可以填补缺失细节并显著提升重建质量。为了推进镜面丰富环境中的三维重建，我们提出了MirrorScene3D，这是一个综合数据集，包含多样化的室内场景、1256张高质量图像以及标注的镜面掩码，为评估反射环境下的重建方法提供了基准。在此基础上，我们提出了ReflectiveGS，这是三维高斯泼溅的一种扩展，将镜面反射作为互补视角而非简单的对称伪影加以利用，从而增强场景几何结构并恢复缺失细节。在MirrorScene3D上的实验表明，ReflectiveGS在SSIM、PSNR、LPIPS和训练速度方面均优于现有方法，为镜面丰富环境中的三维重建设立了新的基准。
英文摘要：
Mirror-containing environments pose unique challenges for 3D reconstruction and novel view synthesis (NVS), as reflective surfaces introduce view-dependent distortions and inconsistencies. While cutting-edge methods such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) excel in typical scenes, their performance deteriorates in the presence of mirrors. Existing solutions mainly focus on handling mirror surfaces through symmetry mapping but often overlook the rich information carried by mirror reflections. These reflections offer complementary perspectives that can fill in absent details and significantly enhance reconstruction quality. To advance 3D reconstruction in mirror-rich environments, we present MirrorScene3D, a comprehensive dataset featuring diverse indoor scenes, 1256 high-quality images, and annotated mirror masks, providing a benchmark for evaluating reconstruction methods in reflective settings. Building on this, we propose ReflectiveGS, an extension of 3D Gaussian Splatting that utilizes mirror reflections as complementary viewpoints rather than simple symmetry artifacts, enhancing scene geometry and recovering absent details. Experiments on MirrorScene3D show that ReflectiveGaussian outperforms existing methods in SSIM, PSNR, LPIPS, and training speed, setting a new benchmark for 3D reconstruction in mirror-rich environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
