In recent years, supervised or unsupervised learning-based MVS methods
achieved excellent performance compared with traditional methods. However,
these methods only use the probability volume computed by cost volume
regularization to predict reference depths and this manner cannot mine enough
information from the probability volume. Furthermore, the unsupervised methods
usually try to use two-step or additional inputs for training which make the
procedure more complicated. In this paper, we propose the DS-MVSNet, an
end-to-end unsupervised MVS structure with the source depths synthesis. To mine
the information in probability volume, we creatively synthesize the source
depths by splattering the probability volume and depth hypotheses to source
views. Meanwhile, we propose the adaptive Gaussian sampling and improved
adaptive bins sampling approach that improve the depths hypotheses accuracy. On
the other hand, we utilize the source depths to render the reference images and
propose depth consistency loss and depth smoothness loss. These can provide
additional guidance according to photometric and geometric consistency in
different views without additional inputs. Finally, we conduct a series of
experiments on the DTU dataset and Tanks & Temples dataset that demonstrate the
efficiency and robustness of our DS-MVSNet compared with the state-of-the-art
methods.

本文提出 DS-MVSNet，一种端到端的无监督多视点结构，并通过 Adaptive Gaussian Sampling 和 Improved Adaptive Bins Sampling 等方法来提高深度假设的准确性。同时，我们利用源深度来渲染参考图像，并提出了深度一致性损失和深度平滑性损失来提供额外的指导。最后，通过在 DTU 数据集和 Tanks＆Temples 数据集上的一系列实验证明了我们的方法相对于现有方法的效率和稳健性。