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 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 language | English |
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Title of host publication | IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023 |
Pages | 3087-3096 |
Number of pages | 10 |
Publication status | Published - 3 Jan 2023 |
Event | 23rd IEEE/CVF Winter Conference on Applications of Computer Vision: WACV 2023 - Waikoloa, United States Duration: 3 Jan 2023 → 7 Jan 2023 https://wacv2023.thecvf.com/home |
Conference
Conference | 23rd IEEE/CVF Winter Conference on Applications of Computer Vision |
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Abbreviated title | WACV 2023 |
Country/Territory | United States |
City | Waikoloa |
Period | 3/01/23 → 7/01/23 |
Internet address |