  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.
