In this paper, we propose a new approach for dense depth estimation based on multimodal stereo images. Our approach employs a combined cost function utilizing robust metrics and a transformation to an illumination independent representation. Additionally, we present a confidence based weighting scheme which allows a pixel-wise weight adjustment within the cost function. We demonstrate the capabilities of our approach using RGB- and thermal images. The resulting depth maps are evaluated by comparing them to depth measurements of a Velodyne HDL-64E LiDAR sensor. We show that our method outperforms current state of the art dense matching methods regarding depth estimation based on multimodal input images.
CITATION STYLE
Mehltretter, M., Kleinschmidt, S. P., Wagner, B., & Heipke, C. (2019). Multimodal Dense Stereo Matching. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11269 LNCS, pp. 407–421). Springer Verlag. https://doi.org/10.1007/978-3-030-12939-2_28
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