Shape from shading (SfS) and stereo are two fundamentally different strategies for image-based 3-D reconstruction. While approaches for SfS infer the depth solely from pixel intensities, methods for stereo are based on a matching process that establishes correspondences across images. This difference in approaching the reconstruction problem yields complementary advantages that are worthwhile being combined. So far, however, most “joint” approaches are based on an initial stereo mesh that is subsequently refined using shading information. In this paper we follow a completely different approach. We propose a joint variational method that combines both cues within a single minimisation framework. To this end, we fuse a Lambertian SfS approach with a robust stereo model and supplement the resulting energy functional with a detail-preserving anisotropic second-order smoothness term. Moreover, we extend the resulting model in such a way that it jointly estimates depth, albedo and illumination. This in turn makes the approach applicable to objects with non-uniform albedo as well as to scenes with unknown illumination. Experiments for synthetic and real-world images demonstrate the benefits of our combined approach: They not only show that our method is capable of generating very detailed reconstructions, but also that joint approaches are feasible in practice.
%0 Journal Article
%1 journals/ijcv/MaurerJBB18
%A Maurer, Daniel
%A Ju, Yong Chul
%A Breuß, Michael
%A Bruhn, Andrés
%D 2018
%J International Journal of Computer Vision
%K 2018 B04 from:leonkokkoliadis sfbtrr161 visus visus:bruhnas visus:maurerdl
%N 12
%P 1342-1366
%R 10.1007/s11263-018-1079-1
%T Combining Shape from Shading and Stereo: A Joint Variational Method for Estimating Depth, Illumination and Albedo
%U https://doi.org/10.1007/s11263-018-1079-1
%V 126
%X Shape from shading (SfS) and stereo are two fundamentally different strategies for image-based 3-D reconstruction. While approaches for SfS infer the depth solely from pixel intensities, methods for stereo are based on a matching process that establishes correspondences across images. This difference in approaching the reconstruction problem yields complementary advantages that are worthwhile being combined. So far, however, most “joint” approaches are based on an initial stereo mesh that is subsequently refined using shading information. In this paper we follow a completely different approach. We propose a joint variational method that combines both cues within a single minimisation framework. To this end, we fuse a Lambertian SfS approach with a robust stereo model and supplement the resulting energy functional with a detail-preserving anisotropic second-order smoothness term. Moreover, we extend the resulting model in such a way that it jointly estimates depth, albedo and illumination. This in turn makes the approach applicable to objects with non-uniform albedo as well as to scenes with unknown illumination. Experiments for synthetic and real-world images demonstrate the benefits of our combined approach: They not only show that our method is capable of generating very detailed reconstructions, but also that joint approaches are feasible in practice.
@article{journals/ijcv/MaurerJBB18,
abstract = {Shape from shading (SfS) and stereo are two fundamentally different strategies for image-based 3-D reconstruction. While approaches for SfS infer the depth solely from pixel intensities, methods for stereo are based on a matching process that establishes correspondences across images. This difference in approaching the reconstruction problem yields complementary advantages that are worthwhile being combined. So far, however, most “joint” approaches are based on an initial stereo mesh that is subsequently refined using shading information. In this paper we follow a completely different approach. We propose a joint variational method that combines both cues within a single minimisation framework. To this end, we fuse a Lambertian SfS approach with a robust stereo model and supplement the resulting energy functional with a detail-preserving anisotropic second-order smoothness term. Moreover, we extend the resulting model in such a way that it jointly estimates depth, albedo and illumination. This in turn makes the approach applicable to objects with non-uniform albedo as well as to scenes with unknown illumination. Experiments for synthetic and real-world images demonstrate the benefits of our combined approach: They not only show that our method is capable of generating very detailed reconstructions, but also that joint approaches are feasible in practice.},
added-at = {2020-03-05T11:43:31.000+0100},
author = {Maurer, Daniel and Ju, Yong Chul and Breuß, Michael and Bruhn, Andrés},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2fcf19bdcb324265a77d726ec973881b0/sfbtrr161},
doi = {10.1007/s11263-018-1079-1},
ee = {https://doi.org/10.1007/s11263-018-1079-1},
interhash = {f3c722deb85c743ae124824ced748d18},
intrahash = {fcf19bdcb324265a77d726ec973881b0},
journal = {International Journal of Computer Vision},
keywords = {2018 B04 from:leonkokkoliadis sfbtrr161 visus visus:bruhnas visus:maurerdl},
number = 12,
pages = {1342-1366},
timestamp = {2020-10-05T11:39:08.000+0200},
title = {Combining Shape from Shading and Stereo: A Joint Variational Method for Estimating Depth, Illumination and Albedo},
url = {https://doi.org/10.1007/s11263-018-1079-1},
volume = 126,
year = 2018
}
