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

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 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.