$α$Surf：解耦几何与不透明度的半透明及薄物体隐式表面重建
$α$Surf: Implicit Surface Reconstruction for Semi-Transparent and Thin Objects with Decoupled Geometry and Opacity
AI总结：
提出$α$Surf，一种解耦几何与不透明度的隐式表面表示，通过解析求解射线-表面交点，实现半透明和薄物体的高质量重建，优于现有SDF和NeRF方法。
摘要：
隐式表面表示，如符号距离函数（SDF），已成为基于图像表面重建的一种有前景的方法。然而，现有的优化方法假设固体表面，因此无法正确重建半透明表面和薄结构，这些结构由于与背景的混合效应而表现出低不透明度。虽然基于神经辐射场（NeRF）的方法可以建模半透明性并在合成的新视角中实现照片级真实感质量，但其体积几何表示紧密耦合了几何和不透明度，因此在不引入伪影的情况下难以轻松转换为表面。我们提出了$α$Surf，一种新颖的表面表示，具有解耦的几何和不透明度，用于重建颜色混合的半透明和薄表面。我们的表示上的射线-表面交点可以通过三次多项式的解析解以封闭形式找到，避免了蒙特卡洛采样，并且通过构造完全可微。我们的定性和定量评估表明，我们的方法可以准确重建具有半透明和薄部分的表面，且伪影更少，实现了比最先进的SDF和NeRF方法更好的重建质量。网站：https://alphasurf.netlify.app/
英文摘要：
Implicit surface representations such as the signed distance function (SDF) have emerged as a promising approach for image-based surface reconstruction. However, existing optimization methods assume solid surfaces and are therefore unable to properly reconstruct semi-transparent surfaces and thin structures, which also exhibit low opacity due to the blending effect with the background. While neural radiance field (NeRF) based methods can model semi-transparency and achieve photo-realistic quality in synthesized novel views, their volumetric geometry representation tightly couples geometry and opacity, and therefore cannot be easily converted into surfaces without introducing artifacts. We present $α$Surf, a novel surface representation with decoupled geometry and opacity for the reconstruction of semi-transparent and thin surfaces where the colors mix. Ray-surface intersections on our representation can be found in closed-form via analytical solutions of cubic polynomials, avoiding Monte-Carlo sampling and is fully differentiable by construction. Our qualitative and quantitative evaluations show that our approach can accurately reconstruct surfaces with semi-transparent and thin parts with fewer artifacts, achieving better reconstruction quality than state-of-the-art SDF and NeRF methods. Website: https://alphasurf.netlify.app/
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。
