Accurate recovery of 3D geometrical surfaces from calibrated 2D multi-view
images is a fundamental yet active research area in computer vision. Despite
the steady progress in multi-view stereo reconstruction, most existing methods
are still limited in recovering fine-scale details and sharp features while
suppressing noises, and may fail in reconstructing regions with few textures.
To address these limitations, this paper presents a Detail-preserving and
Content-aware Variational (DCV) multi-view stereo method, which reconstructs
the 3D surface by alternating between reprojection error minimization and mesh
denoising. In reprojection error minimization, we propose a novel inter-image
similarity measure, which is effective to preserve fine-scale details of the
reconstructed surface and builds a connection between guided image filtering
and image registration. In mesh denoising, we propose a content-aware
$\ell_{p}$-minimization algorithm by adaptively estimating the $p$ value and
regularization parameters based on the current input. It is much more promising
in suppressing noise while preserving sharp features than conventional
isotropic mesh smoothing. Experimental results on benchmark datasets
demonstrate that our DCV method is capable of recovering more surface details,
and obtains cleaner and more accurate reconstructions than state-of-the-art
methods. In particular, our method achieves the best results among all
published methods on the Middlebury dino ring and dino sparse ring datasets in
terms of both completeness and accuracy.

本文研究了多视角立体重建中的细节保持和内容感知不全变分（DCV）方法，该方法通过交替进行重投影误差最小化和网格去噪，利用图像滤波和图像配准建立了一种新的图像相似度度量，能够有效地保留表面的细节特征，同时提出了一种适应性估计方法，能够更好地抑制噪声并保持尖锐的特征。实验结果表明，DCV 方法能够比现有的方法更好地恢复更多的表面细节，并获得比现有技术更干净和更准确的重建。