PISR：面向无纹理镜面物体的偏振神经隐式表面重建
PISR: Polarimetric Neural Implicit Surface Reconstruction for Textureless and Specular Objects
AI总结：
本文提出PISR方法，利用偏振损失独立于外观细化无纹理镜面物体形状，结合图像空间法线平滑与哈希网格神经SDF加速重建，实现更高精度与4-10倍收敛加速。
摘要：
神经隐式表面重建近期取得了显著进展。尽管采用了复杂的光照建模，最先进的方法在处理无纹理和镜面表面时仍存在困难。与RGB图像不同，偏振图像可以为表面法线的方位角提供直接约束。在本文中，我们提出了PISR，一种利用几何精确的偏振损失独立于外观来细化形状的新方法。此外，PISR在图像空间中平滑表面法线以消除严重的几何畸变，并利用基于哈希网格的神经符号距离函数来加速重建。实验结果表明，PISR实现了更高的精度和鲁棒性，L1倒角距离为0.5 mm，F-score为99.5%，收敛速度比以前的偏振表面重建方法快4到10倍。
英文摘要：
Neural implicit surface reconstruction has achieved remarkable progress recently. Despite resorting to complex radiance modeling, state-of-the-art methods still struggle with textureless and specular surfaces. Different from RGB images, polarization images can provide direct constraints on the azimuth angles of the surface normals. In this paper, we present PISR, a novel method that utilizes a geometrically accurate polarimetric loss to refine shape independently of appearance. In addition, PISR smooths surface normals in image space to eliminate severe shape distortions and leverages the hash-grid-based neural signed distance function to accelerate the reconstruction. Experimental results demonstrate that PISR achieves higher accuracy and robustness, with an L1 Chamfer distance of 0.5 mm and an F-score of 99.5% at 1 mm, while converging 4~30 times faster than previous polarimetric surface reconstruction methods.
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。
