DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo
Christian Sormann (1), Emanuele Santellani (1), Mattia Rossi (2),, Andreas Kuhn (2), Friedrich Fraundorfer (1) ((1) Graz University of, Technology, (2) Sony Europe B.V.)

TL;DR
DELS-MVS introduces an epipolar line search method for multi-view stereo that avoids depth range discretization, enabling more even and efficient depth estimation with robust fusion, achieving competitive results.
Contribution
The paper presents a novel deep learning-based MVS approach that performs direct epipolar line search, eliminating the need for depth range discretization and costly cost volume construction.
Findings
Achieves competitive results on ETH3D, Tanks and Temples, and DTU benchmarks.
Avoids depth range selection and discretization, simplifying the MVS pipeline.
Performs robust, geometry-aware depth map fusion.
Abstract
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…
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Videos
DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo· youtube
Taxonomy
TopicsAdvanced Vision and Imaging · Advanced Image Processing Techniques · Image Processing Techniques and Applications
MethodsTest
