Multi-Scale Surface Normal Estimation from Depth Maps

Diclehan Ulucan, Oguzhan Ulucan, Marc Ebner

2023

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.

Paper Citation

in Harvard Style

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 - Volume 1: IMPROVE, ISBN 978-989-758-642-2, SciTePress, pages 47-56. DOI: 10.5220/0011968300003497

in Bibtex Style

@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 - Volume 1: IMPROVE,},
year={2023},
pages={47-56},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011968300003497},
isbn={978-989-758-642-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,
TI - Multi-Scale Surface Normal Estimation from Depth Maps
SN - 978-989-758-642-2
AU - Ulucan D.
AU - Ulucan O.
AU - Ebner M.
PY - 2023
SP - 47
EP - 56
DO - 10.5220/0011968300003497
PB - SciTePress