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Institut für Visualisierung und Interaktive Systeme
Team
Daniel Maurer

Daniel Maurer

Herr Dr.

Institut für Visualisierung und Interaktive Systeme (VIS)

Kontakt

Visitenkarte (VCF)

Deutschland

Publikationen

Publikationen:

2019
1. Men, Hui ; Lin, Hanhe ; Hosu, Vlad ; Maurer, Daniel ; Bruhn, Andrés ; Saupe, Dietmar: Visual Quality Assessment for Motion Compensated Frame Interpolation. In: Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX) : IEEE, 2019 — ISBN 978-1-5386-8212-8, S. 1–6
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  BibTeX
  @inproceedings{conf/qomex/MenLHMBS19, abstract = {Current benchmarks for optical flow algorithms evaluate the estimation quality by comparing their predicted flow field with the ground truth, and additionally may compare interpolated frames, based on these predictions, with the correct frames from the actual image sequences. For the latter comparisons, objective measures such as mean square errors are applied. However, for applications like image interpolation, the expected user's quality of experience cannot be fully deduced from such simple quality measures. Therefore, we conducted a subjective quality assessment study by crowdsourcing for the interpolated images provided in one of the optical flow benchmarks, the Middlebury benchmark. We used paired comparisons with forced choice and reconstructed absolute quality scale values according to Thurstone's model using the classical least squares method. The results give rise to a re-ranking of 141 participating algorithms w.r.t. visual quality of interpolated frames mostly based on optical flow estimation. Our re-ranking result shows the necessity of visual quality assessment as another evaluation metric for optical flow and frame interpolation benchmarks.}, author = {Men, Hui and Lin, Hanhe and Hosu, Vlad and Maurer, Daniel and Bruhn, Andrés and Saupe, Dietmar}, booktitle = {Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX)}, doi = {10.1109/QoMEX.2019.8743221}, ee = {https://doi.org/10.1109/QoMEX.2019.8743221}, isbn = {978-1-5386-8212-8}, pages = {1-6}, publisher = {IEEE}, title = {Visual Quality Assessment for Motion Compensated Frame Interpolation}, url = {https://ieeexplore.ieee.org/document/8743221}, year = 2019 }
  Link
  https://ieeexplore.ieee.org/document/8743221
2018
1. Maurer, Daniel ; Marniok, Nico ; Goldluecke, Bastian ; Bruhn, Andrés: Structure-from-motion-aware PatchMatch for Adaptive Optical Flow Estimation. In: Ferrari, V. ; Hebert, M. ; Sminchisescu, C. ; Weiss, Y. (Hrsg.) ; Ferrari, V. ; Hebert, M. ; Sminchisescu, C. ; Weiss, Y. (Hrsg.): Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science. Bd. 11212 : Springer International Publishing, 2018 — ISBN 978-3-030-01237-3, S. 575–592
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  @inbook{conf/eccv/MaurerMGB18, abstract = {Many recent energy-based methods for optical flow estimation rely on a good initialization that is typically provided by some kind of feature matching. So far, however, these initial matching approaches are rather general: They do not incorporate any additional information that could help to improve the accuracy or the robustness of the estimation. In particular, they do not exploit potential cues on the camera poses and the thereby induced rigid motion of the scene. In the present paper, we tackle this problem. To this end, we propose a novel structure-from-motion-aware PatchMatch approach that, in contrast to existing matching techniques, combines two hierarchical feature matching methods: a recent two-frame PatchMatch approach for optical flow estimation (general motion) and a specifically tailored three-frame PatchMatch approach for rigid scene reconstruction (SfM). While the motion PatchMatch serves as baseline with good accuracy, the SfM counterpart takes over at occlusions and other regions with insufficient information. Experiments with our novel SfM-aware PatchMatch approach demonstrate its usefulness. They not only show excellent results for all major benchmarks (KITTI 2012/2015, MPI Sintel), but also improvements up to 50% compared to a PatchMatch approach without structure information.}, author = {Maurer, Daniel and Marniok, Nico and Goldluecke, Bastian and Bruhn, Andrés}, booktitle = {Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science}, doi = {10.1007/978-3-030-01237-3_35}, editor = {Ferrari, Vittorio and Hebert, Martial and Sminchisescu, Cristian and Weiss, Yair}, ee = {https://doi.org/10.1007/978-3-030-01237-3_35}, isbn = {978-3-030-01237-3}, pages = {575-592}, publisher = {Springer International Publishing}, title = {Structure-from-motion-aware PatchMatch for Adaptive Optical Flow Estimation}, url = {https://doi.org/10.1007/978-3-030-01237-3_35 }, volume = 11212, year = 2018 }
  Link
  https://doi.org/10.1007/978-3-030-01237-3_35
2. Maurer, Daniel ; Bruhn, Andrés: ProFlow: Learning to Predict Optical Flow. In: Proceedings of the British Machine Vision Conference (BMVC), Proceedings of the British Machine Vision Conference (BMVC). Bd. 86:1-86:13 : BMVA Press, 2018
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  @inproceedings{noauthororeditor2018proflow, abstract = {Temporal coherence is a valuable source of information in the context of optical flow estimation. However, finding a suitable motion model to leverage this information is a non-trivial task. In this paper we propose an unsupervised online learning approach based on a convolutional neural network (CNN) that estimates such a motion model individually for each frame. By relating forward and backward motion these learned models not only allow to infer valuable motion information based on the backward flow, they also help to improve the performance at occlusions, where a reliable prediction is particularly useful. Moreover, our learned models are spatially variant and hence allow to estimate non-rigid motion per construction. This, in turns, allows to overcome the major limitation of recent rigidity-based approaches that seek to improve the estimation by incorporating additional stereo/SfM constraints. Experiments demonstrate the usefulness of our new approach. They not only show a consistent improvement of up to 27% for all major benchmarks (KITTI 2012, KITTI 2015, MPI Sintel) compared to a baseline without prediction, they also show top results for the MPI Sintel benchmark -- the one of the three benchmarks that contains the largest amount of non-rigid motion.}, author = {Maurer, Daniel and Bruhn, Andrés}, booktitle = {Proceedings of the British Machine Vision Conference (BMVC)}, doi = {arXiv:1806.00800}, publisher = {BMVA Press}, title = {ProFlow: Learning to Predict Optical Flow}, url = {http://bmvc2018.org/contents/supplementary/pdf/0277_supp.pdf}, volume = {86:1-86:13}, year = 2018 }
  Link
  http://bmvc2018.org/contents/supplementary/pdf/0277_supp.pdf
3. Maurer, Daniel ; Ju, Yong Chul ; Breuß, Michael ; Bruhn, Andrés: Combining Shape from Shading and Stereo: A Joint Variational Method for Estimating Depth, Illumination and Albedo. In: International Journal of Computer Vision, International Journal of Computer Vision. Bd. 126 (2018), Nr. 12, S. 1342–1366
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  @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.}, author = {Maurer, Daniel and Ju, Yong Chul and Breuß, Michael and Bruhn, Andrés}, doi = {10.1007/s11263-018-1079-1}, ee = {https://doi.org/10.1007/s11263-018-1079-1}, journal = {International Journal of Computer Vision}, number = 12, pages = {1342-1366}, 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 }
  Link
  https://doi.org/10.1007/s11263-018-1079-1
4. Maurer, Daniel ; Stoll, Michael ; Bruhn, Andrés: Directional Priors for Multi-Frame Optical Flow. In: Proceedings of the British Machine Vision Conference (BMVC), Proceedings of the British Machine Vision Conference (BMVC) : BMVA Press, 2018, S. 106:1-106:13
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  @inproceedings{conf/bmvc/MaurerSB18, abstract = {Pipeline approaches that interpolate and refine an initial set of point correspondenceshave recently shown a good performance in the field of optical flow estimation. However,so far, these methods are typically restricted to two frames which makes exploiting tem-poral information difficult. In this paper, we show how such pipeline approaches can beextended to the temporal domain and how directional constraints can be incorporated tofurther improve the estimation. To this end, we not only suggest to exploit temporal infor-mation in the prefiltering step, we also propose a trajectorial refinement method that liftssuccessful concepts of recent variational two-frame methods to the multi-frame domain.Experiments demonstrate the usefulness of our pipeline approach. They do not only showgood results in general, they also demonstrate the clear benefits of using multiple framesand of imposing directional constraints on the prefiltering step and the refinement.}, author = {Maurer, Daniel and Stoll, Michael and Bruhn, Andrés}, booktitle = {Proceedings of the British Machine Vision Conference (BMVC)}, crossref = {conf/bmvc/2018}, ee = {http://bmvc2018.org/contents/papers/0377.pdf}, pages = {106:1-106:13}, publisher = {BMVA Press}, title = {Directional Priors for Multi-Frame Optical Flow}, url = {http://bmvc2018.org/contents/papers/0377.pdf}, year = 2018 }
  Link
  http://bmvc2018.org/contents/papers/0377.pdf
2017
1. Stoll, Michael ; Maurer, Daniel ; Volz, Sebastian ; Bruhn, Andrés: Illumination-Aware Large Displacement Optical Flow. In: Hancock, E. ; Pelillo, M. (Hrsg.) ; Hancock, E. ; Pelillo, M. (Hrsg.): Proceedings of International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR). Lecture Notes in Computer Science, Proceedings of International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR). Lecture Notes in Computer Science : Springer, 2017
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  @inproceedings{d7910e5a7878417aaddc8b3c8891b40b, author = {Stoll, Michael and Maurer, Daniel and Volz, Sebastian and Bruhn, Andrés}, booktitle = {Proceedings of International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR). Lecture Notes in Computer Science}, editor = {Hancock, E. and Pelillo, M.}, publisher = {Springer}, title = {Illumination-Aware Large Displacement Optical Flow}, year = 2017 }
2. Stoll, Michael ; Volz, Sebastian ; Maurer, Daniel ; Bruhn, Andrés: A Time-Efficient Optimisation Framework for Parameters of Opitical Flow Methods. In: Jenssen, R. ; Sharma, P. ; Bianchi, F. M. (Hrsg.) ; Jenssen, R. ; Sharma, P. ; Bianchi, F. M. (Hrsg.): Proceedings of Scandinavian Conference on Image Analysis (SCIA). Lecture Notes in Computer Science, Proceedings of Scandinavian Conference on Image Analysis (SCIA). Lecture Notes in Computer Science. Bd. 10269 : Springer, 2017
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  @inproceedings{008e9cd783b44dbeba802331440d7f1e, author = {Stoll, Michael and Volz, Sebastian and Maurer, Daniel and Bruhn, Andrés}, booktitle = {Proceedings of Scandinavian Conference on Image Analysis (SCIA). Lecture Notes in Computer Science}, editor = {Jenssen, R. and Sharma, P. and Bianchi, F. M.}, privnote = {uid:2398}, publisher = {Springer}, title = {A Time-Efficient Optimisation Framework for Parameters of Opitical Flow Methods}, volume = 10269, year = 2017 }
3. Maurer, Daniel ; Stoll, Michael ; Volz, Sebastian ; Gairing, Patrick ; Bruhn, Andrés: A Comparison of Isotropic and Anisotropic Second Order Regularisers for Optical Flow. In: Dahl, A.-B. ; Dong, Y. ; Lauze, F. (Hrsg.) ; Dahl, A.-B. ; Dong, Y. ; Lauze, F. (Hrsg.): Proceedings of International Conference on Scale Space and Variational Methods in Computer Vision (SSVM). Lecture Notes in Computer Science, Proceedings of International Conference on Scale Space and Variational Methods in Computer Vision (SSVM). Lecture Notes in Computer Science. Bd. 10302 : Springer, 2017
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  @inproceedings{d7fa64b192974cd9acff01d05fc1f9c5, author = {Maurer, Daniel and Stoll, Michael and Volz, Sebastian and Gairing, Patrick and Bruhn, Andrés}, booktitle = {Proceedings of International Conference on Scale Space and Variational Methods in Computer Vision (SSVM). Lecture Notes in Computer Science}, editor = {Dahl, A.-B. and Dong, Y. and Lauze, F.}, privnote = {uid:2396}, publisher = {Springer}, title = {A Comparison of Isotropic and Anisotropic Second Order Regularisers for Optical Flow}, volume = 10302, year = 2017 }
4. Maurer, Daniel ; Bruhn, Andrés ; Stoll, Michael: Order-Adaptive and Illumination-Aware Variational Optical Flow Refinement. In: Proceedings of the British Machine Vision Conference (BMVC), Proceedings of the British Machine Vision Conference (BMVC) : BMVA Press, 2017
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  @inproceedings{bc819c3f13034a82bb8023234baa091b, author = {Maurer, Daniel and Bruhn, Andrés and Stoll, Michael}, booktitle = {Proceedings of the British Machine Vision Conference (BMVC)}, privnote = {uid:2395}, publisher = {BMVA Press}, title = {Order-Adaptive and Illumination-Aware Variational Optical Flow Refinement}, year = 2017 }
5. Stoll, Michael ; Maurer, Daniel ; Bruhn, Andrés: Variational Large Displacement Optical Flow without Feature Matches. In: Hancock, E. ; Pelillo, M. (Hrsg.) ; Hancock, E. ; Pelillo, M. (Hrsg.): Proceedings of International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR). Lecture Notes in Computer Science, Proceedings of International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR). Lecture Notes in Computer Science : Springer, 2017
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  @inproceedings{ccf6bef5b98e4bfcb6b858b5eb0b18bb, author = {Stoll, Michael and Maurer, Daniel and Bruhn, Andrés}, booktitle = {Proceedings of International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR). Lecture Notes in Computer Science}, editor = {Hancock, E. and Pelillo, M.}, publisher = {Springer}, title = {Variational Large Displacement Optical Flow without Feature Matches}, year = 2017 }
6. Maurer, Daniel ; Stoll, Michael ; Bruhn, Andrés: Order-Adaptive Regularisation for Variational Optical Flow: Global, Local and in Between. In: Dahl, A.-B. ; Dong, Y. ; Lauze, F. (Hrsg.) ; Dahl, A.-B. ; Dong, Y. ; Lauze, F. (Hrsg.): Proceedings of International Conference on Scale Space and Variational Methods in Computer Vision (SSVM). Lecture Notes in Computer Science, Proceedings of International Conference on Scale Space and Variational Methods in Computer Vision (SSVM). Lecture Notes in Computer Science. Bd. 10302 : Springer, 2017
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  BibTeX
  @inproceedings{3d4120cde94d4657b613d5d2a4e59fdf, author = {Maurer, Daniel and Stoll, Michael and Bruhn, Andrés}, booktitle = {Proceedings of International Conference on Scale Space and Variational Methods in Computer Vision (SSVM). Lecture Notes in Computer Science}, editor = {Dahl, A.-B. and Dong, Y. and Lauze, F.}, privnote = {uid:2397}, publisher = {Springer}, title = {Order-Adaptive Regularisation for Variational Optical Flow: Global, Local and in Between}, volume = 10302, year = 2017 }
2016
1. Ju, Yong-Chul ; Maurer, Daniel ; Breuß, Michael ; Bruhn, Andrés: Direct Variational Perspective Shape from Shading with Cartesian Depth Parametrisation. In: Breuß, M. ; Maragos, P. ; Wuhrer, S. (Hrsg.) ; Breuß, M. ; Maragos, P. ; Wuhrer, S. (Hrsg.): Perspectives on Shape From Shading. Mathematics and Visualization, Perspectives on Shape From Shading. Mathematics and Visualization : Springer, 2016
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  @inbook{fcb8069f2bf44b9e9dac261ae0c667b6, author = {Ju, Yong-Chul and Maurer, Daniel and Breuß, Michael and Bruhn, Andrés}, booktitle = {Perspectives on Shape From Shading. Mathematics and Visualization}, editor = {Breuß, M. and Maragos, P. and Wuhrer, S.}, privnote = {uid:2135}, publisher = {Springer}, title = {Direct Variational Perspective Shape from Shading with Cartesian Depth Parametrisation}, year = 2016 }
2. Maurer, Daniel ; Ju, Yong-Chul ; Breuß, Michael ; Bruhn, Andrés: Combining shape from shading and stereo: a variational approach for the joint estimation of depth, illumination and albedo. In: Proceedings of the British Machine Vision Conference (BMVC), Proceedings of the British Machine Vision Conference (BMVC) : BMVA Press, 2016
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  @inproceedings{b45aa68a780d40d3a048e310f4ca89e3, author = {Maurer, Daniel and Ju, Yong-Chul and Breuß, Michael and Bruhn, Andrés}, booktitle = {Proceedings of the British Machine Vision Conference (BMVC)}, publisher = {BMVA Press}, title = {Combining shape from shading and stereo: a variational approach for the joint estimation of depth, illumination and albedo.}, year = 2016 }
2015
1. Maurer, Daniel ; Ju, Yong-Chul ; Breuß, Michael ; Bruhn, Andrés: An Efficient Linearisation Approach for Variational Perspective Shape from Shading. In: Gall, J. ; Gehler, P. ; Leibe, B. (Hrsg.) ; Gall, J. ; Gehler, P. ; Leibe, B. (Hrsg.): German Conference on Pattern Recognition (GCPR 2015). Lecture Notes in Computer Science, German Conference on Pattern Recognition (GCPR 2015). Lecture Notes in Computer Science. Bd. 9358 : Springer, 2015
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  @inproceedings{4a1f5015d0624c3b98d5bcad8f33be03, author = {Maurer, Daniel and Ju, Yong-Chul and Breuß, Michael and Bruhn, Andrés}, booktitle = {German Conference on Pattern Recognition (GCPR 2015). Lecture Notes in Computer Science}, editor = {Gall, J. and Gehler, P. and Leibe, B.}, privnote = {uid:2134}, publisher = {Springer}, title = {An Efficient Linearisation Approach for Variational Perspective Shape from Shading}, url = {http://dx.doi.org/10.1007/978-3-319-24947-6_20}, volume = 9358, year = 2015 }
  Link
  http://dx.doi.org/10.1007/978-3-319-24947-6_20

Daniel Maurer

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2019

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2017

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Daniel Maurer

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2019

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2018

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2017

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