Multi-View Stereo~(MVS) is a fundamental problem in geometric computer vision
which aims to reconstruct a scene using multi-view images with known camera
parameters. However, the mainstream approaches represent the scene with a fixed
all-pixel depth range and equal depth interval partition, which will result in
inadequate utilization of depth planes and imprecise depth estimation. In this
paper, we present a novel multi-stage coarse-to-fine framework to achieve
adaptive all-pixel depth range and depth interval. We predict a coarse depth
map in the first stage, then an Adaptive Depth Range Prediction module is
proposed in the second stage to zoom in the scene by leveraging the reference
image and the obtained depth map in the first stage and predict a more accurate
all-pixel depth range for the following stages. In the third and fourth stages,
we propose an Adaptive Depth Interval Adjustment module to achieve adaptive
variable interval partition for pixel-wise depth range. The depth interval
distribution in this module is normalized by Z-score, which can allocate dense
depth hypothesis planes around the potential ground truth depth value and vice
versa to achieve more accurate depth estimation. Extensive experiments on four
widely used benchmark datasets~(DTU, TnT, BlendedMVS, ETH 3D) demonstrate that
our model achieves state-of-the-art performance and yields competitive
generalization ability. Particularly, our method achieves the highest Acc and
Overall on the DTU dataset, while attaining the highest Recall and
$F_{1}$-score on the Tanks and Temples intermediate and advanced dataset.
Moreover, our method also achieves the lowest $e_{1}$ and $e_{3}$ on the
BlendedMVS dataset and the highest Acc and $F_{1}$-score on the ETH 3D dataset,
surpassing all listed methods.Project website:
this https URL

该研究提出了一种新颖的多阶段粗到细的框架，用于实现自适应全像素深度范围和深度间隔的多视图立体重建，通过预测粗糙深度图、自适应深度范围预测和自适应深度间隔调整模块，可以实现更准确的深度估计。实验结果表明该模型在多个基准数据集上取得了最先进的性能，并具备较强的泛化能力。