We propose a fast and generalizable solution to Multi-view Photometric Stereo
(MVPS), called MVPSNet. The key to our approach is a feature extraction network
that effectively combines images from the same view captured under multiple
lighting conditions to extract geometric features from shading cues for stereo
matching. We demonstrate these features, termed `Light Aggregated Feature Maps'
(LAFM), are effective for feature matching even in textureless regions, where
traditional multi-view stereo methods fail. Our method produces similar
reconstruction results to PS-NeRF, a state-of-the-art MVPS method that
optimizes a neural network per-scene, while being 411$\times$ faster (105
seconds vs. 12 hours) in inference. Additionally, we introduce a new synthetic
dataset for MVPS, sMVPS, which is shown to be effective to train a
generalizable MVPS method.

我们提出了一个快速且通用的解决多视图光度立体 (MVPS) 问题的方法，称为 MVPSNet。我们的方法通过一个特征提取网络有效地结合同一视图下不同光照条件下的图像，从阴影线索中提取几何特征用于立体匹配。我们通过介绍一种新的人工合成数据集 sMVPS 来训练我们的方法，并展示了在纹理缺失区域中提取特征的有效性。结果表明，我们的方法的重建结果与一个最先进的 MVPS 方法 PS-NeRF 相似，但推理速度快 411 倍（105 秒与 12 小时），具有一般化的可训练能力。

MVPSNet：快速通用的多视角光度立体

MVPSNet: Fast Generalizable Multi-view Photometric Stereo

Deep learning-based multi-view stereo has emerged as a powerful paradigm for
reconstructing the complete geometrically-detailed objects from multi-views.
Most of the existing approaches only estimate the pixel-wise depth value by
minimizing the gap between the predicted point and the intersection of ray and
surface, which usually ignore the surface topology. It is essential to the
textureless regions and surface boundary that cannot be properly reconstructed.
To address this issue, we suggest to take advantage of point-to-surface
distance so that the model is able to perceive a wider range of surfaces. To
this end, we predict the distance volume from cost volume to estimate the
signed distance of points around the surface. Our proposed RA-MVSNet is
patch-awared, since the perception range is enhanced by associating
hypothetical planes with a patch of surface. Therefore, it could increase the
completion of textureless regions and reduce the outliers at the boundary.
Moreover, the mesh topologies with fine details can be generated by the
introduced distance volume. Comparing to the conventional deep learning-based
multi-view stereo methods, our proposed RA-MVSNet approach obtains more
complete reconstruction results by taking advantage of signed distance
supervision. The experiments on both the DTU and Tanks \& Temples datasets
demonstrate that our proposed approach achieves the state-of-the-art results.

本研究使用深度学习等技术，提出了一个基于点到表面距离的补丁感知（patch-awared）多视角立体重建（multi-view stereo）方法，能够更好地重建纹理缺失区域和表面边界，并且经过实验证明，在 DTU 和 Tanks & Temples 数据集上达到了最佳重建效果。