  We propose a novel approach for deep learning-based Multi-View Stereo (MVS).
For each pixel in the reference image, our method leverages a deep architecture
to search for the corresponding point in the source image directly along the
corresponding epipolar line. We denote our method DELS-MVS: Deep Epipolar Line
Search Multi-View Stereo. Previous works in deep MVS select a range of interest
within the depth space, discretize it, and sample the epipolar line according
to the resulting depth values: this can result in an uneven scanning of the
epipolar line, hence of the image space. Instead, our method works directly on
the epipolar line: this guarantees an even scanning of the image space and
avoids both the need to select a depth range of interest, which is often not
known a priori and can vary dramatically from scene to scene, and the need for
a suitable discretization of the depth space. In fact, our search is iterative,
which avoids the building of a cost volume, costly both to store and to
process. Finally, our method performs a robust geometry-aware fusion of the
estimated depth maps, leveraging a confidence predicted alongside each depth.
We test DELS-MVS on the ETH3D, Tanks and Temples and DTU benchmarks and achieve
competitive results with respect to state-of-the-art approaches.
