发表机构
Rice University（莱斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究从部分反射视图进行环境重建，提出基于高斯点的PanoLess框架，利用表面对齐二维高斯点和延迟阴影恢复法线与反射线索，生成可见性图，能从部分视图输入实现光照估计，在多数据集上表现出色，实现高保真和几何一致的环境重建。
AI 中文摘要
来自闪亮物体和玻璃幕墙的反射自然地扩展了相机的视野，无需平移相机或获取完整全景图就能捕捉周围环境。我们提出了PanoLess，这是一个基于高斯点的框架，它能从仅在反射表面一侧拍摄的图像中将周围环境重建为远距离光照图。PanoLess利用表面对齐的二维高斯点和延迟阴影来恢复精确的逐像素法线和反射线索，并融合到环境的神经立方体贴图表示中。此外，PanoLess还生成一个可见性图，明确表示部分反射观测支持环境的哪些区域。与现有的逆渲染和反射感知高斯点方法不同，PanoLess能从部分视图输入中实现一致的、基于物理的光照估计。我们表明，PanoLess实现了高保真和几何一致的环境重建，在新的自定义合成基准和公开可用数据集上优于反射感知基线，并证明了对真实世界反射捕捉的泛化能力。
英文摘要
Reflections from shiny objects and glass facades naturally extend the field of view of a camera, capturing the surrounding environment without the need to pan the camera or acquire a full panorama. We propose PanoLess, a Gaussian-splat-based framework that reconstructs the surrounding environment as a distant illumination map from images captured on only one side of a reflective surface. PanoLess leverages surface-aligned 2D Gaussian splats with deferred shading to recover accurate per-pixel normals and reflection cues, which are fused into a neural cubemap representation of the environment. In addition, PanoLess produces a visibility map that explicitly denotes which regions of the environment are supported by the partial reflective observations. Unlike existing inverse-rendering and reflection-aware Gaussian-splatting approaches, which typically require full 360-degree coverage and struggle under incomplete views, PanoLess enables consistent, physically grounded illumination estimation from partial-view input. We show that PanoLess achieves high-fidelity and geometrically consistent environment reconstruction, outperforming reflection-aware baselines on a new custom synthetic benchmark and publicly available datasets, and demonstrating generalization to real-world reflective captures.
CommentsECCV 2026. Main paper with supplementary material
