DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo

Christian Sormann*, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


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.
Original languageEnglish
Title of host publicationIEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023
Number of pages10
Publication statusPublished - 3 Jan 2023
Event23rd IEEE/CVF Winter Conference on Applications of Computer Vision: WACV 2023 - Waikoloa, United States
Duration: 3 Jan 20237 Jan 2023


Conference23rd IEEE/CVF Winter Conference on Applications of Computer Vision
Abbreviated titleWACV 2023
Country/TerritoryUnited States
Internet address


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