TSPE-GS：基于3D高斯溅射的半透明表面重建概率深度提取方法
TSPE-GS: Probabilistic Depth Extraction for Semi-Transparent Surface Reconstruction via 3D Gaussian Splatting
AI总结：
针对3D高斯溅射难以重建半透明表面的问题，提出TSPE-GS方法，通过概率深度建模与渐进式TSDF融合实现半透明内外表面重建，可泛化且无额外训练开销，显著提升半透明场景重建效果。
摘要：
3D高斯溅射（3D Gaussian Splatting）实现了优异的速度与质量权衡，但难以重建半透明表面，原因是多数方法假设每个像素对应单一深度，当存在多个可见表面时该假设便不成立。我们提出TSPE-GS（面向高斯溅射的透明表面概率提取方法），通过均匀采样透射率对逐像素的不透明度与深度多模态分布建模，替换此前的单峰假设，解决跨表面深度歧义问题。通过渐进式融合截断符号距离函数，TSPE-GS可在统一框架内分别重建外表面与内表面。该方法可泛化至其他基于高斯的重建管线，且无需额外训练开销。在公开及自采集的半透明与不透明数据集上开展的大量实验表明，TSPE-GS可显著提升半透明几何重建效果，同时在不透明场景上保持原有性能。
英文摘要：
3D Gaussian Splatting offers a strong speed-quality trade-off but struggles to reconstruct semi-transparent surfaces because most methods assume a single depth per pixel, which fails when multiple surfaces are visible. We propose TSPE-GS (Transparent Surface Probabilistic Extraction for Gaussian Splatting), which uniformly samples transmittance to model a pixel-wise multi-modal distribution of opacity and depth, replacing the prior single-peak assumption and resolving cross-surface depth ambiguity. By progressively fusing truncated signed distance functions, TSPE-GS reconstructs external and internal surfaces separately within a unified framework. The method generalizes to other Gaussian-based reconstruction pipelines without extra training overhead. Extensive experiments on public and self-collected semi-transparent and opaque datasets show TSPE-GS significantly improves semi-transparent geometry reconstruction while maintaining performance on opaque scenes.
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