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Authors: Diclehan Ulucan ; Oguzhan Ulucan and Marc Ebner

Affiliation: Institut für Mathematik und Informatik, Universität Greifswald, Walther-Rathenau-Straße 47, 17489 Greifswald, Germany

Keyword(s): Intrinsic Image Decomposition, Surface Normal Estimation, Depth Map, Scale-Space.

Abstract: Surface normal vectors are important local descriptors of images, which are utilized in many applications in the field of computer vision and computer graphics. Hence, estimating the surface normals from structured range sensor data is an important step for many image processing pipelines. Thereupon, we present a simple yet effective, learning-free surface normal estimation strategy for both complete and incomplete depth maps. The proposed method takes advantage of scale-space. While the finest scale is used for the initial estimations, the missing surface normals, which cannot be estimated properly are filled from the coarser scales of the pyramid. The same procedure is applied for incomplete depth maps with a slight modification, where we guide the algorithm using the gradient information obtained from the shading image of the scene, which has a geometric relationship with the surface normals. In order to test our method for the incomplete depth maps scenario, we augmented the MIT- Berkeley Intrinsic Images dataset by creating two different sets, namely, easy and hard. According to the experiments, the proposed algorithm achieves competitive results on datasets containing both single objects and realistic scenes. (More)

CC BY-NC-ND 4.0

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Paper citation in several formats:
Ulucan, D.; Ulucan, O. and Ebner, M. (2023). Multi-Scale Surface Normal Estimation from Depth Maps. In Proceedings of the 3rd International Conference on Image Processing and Vision Engineering - IMPROVE; ISBN 978-989-758-642-2; ISSN 2795-4943, SciTePress, pages 47-56. DOI: 10.5220/0011968300003497

@conference{improve23,
author={Diclehan Ulucan. and Oguzhan Ulucan. and Marc Ebner.},
title={Multi-Scale Surface Normal Estimation from Depth Maps},
booktitle={Proceedings of the 3rd International Conference on Image Processing and Vision Engineering - IMPROVE},
year={2023},
pages={47-56},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011968300003497},
isbn={978-989-758-642-2},
issn={2795-4943},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Image Processing and Vision Engineering - IMPROVE
TI - Multi-Scale Surface Normal Estimation from Depth Maps
SN - 978-989-758-642-2
IS - 2795-4943
AU - Ulucan, D.
AU - Ulucan, O.
AU - Ebner, M.
PY - 2023
SP - 47
EP - 56
DO - 10.5220/0011968300003497
PB - SciTePress