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spezial:bib [2019/07/29 09:23] wenzel |
spezial:bib [2019/12/03 17:13] Marvin Eisenberger |
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titleurl = {rodola-3dv14.pdf}, | titleurl = {rodola-3dv14.pdf}, | ||
topic = {Correspondence, | topic = {Correspondence, | ||
- | } | ||
- | |||
- | @Misc{demmel14, | ||
- | author = Nikolaus Demmel, | ||
- | title = Total Variation Segmentation Incorporating Depth Information | ||
- | month = Sept., | ||
- | year = 2014 | ||
- | note = IDP Project, | ||
- | keywords = Total Variation, Segmentation, | ||
} | } | ||
Line 7967: | Line 7958: | ||
year = {2019}, | year = {2019}, | ||
note = {(Presented at Symposium on Geometry Processing (SGP)) {<a href=" | note = {(Presented at Symposium on Geometry Processing (SGP)) {<a href=" | ||
+ | } | ||
+ | |||
+ | @inproceedings{sang2020wacv, | ||
+ | title = {Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational Approach}, | ||
+ | author = {Sang, L. and Haefner, B. and Cremers, D.}, | ||
+ | booktitle={IEEE Winter Conference on Applications of Computer Vision (WACV)}, | ||
+ | month={March}, | ||
+ | address={Colorado, | ||
+ | year = {2020}, | ||
+ | award = {}, | ||
+ | titleurl = {sang2020wacv.pdf}, | ||
+ | } | ||
+ | |||
+ | @article{brahimi2019springer, | ||
+ | title = {On well-posedness of uncalibrated photometric stereo under general lighting}, | ||
+ | author = {Brahimi, M. and Quéau, Y. and Haefner, B. and Cremers, D.}, | ||
+ | journal = {arXiv preprint arXiv: | ||
+ | year = {2019}, | ||
+ | eprint = {1911.07268}, | ||
+ | eprinttype = {arXiv}, | ||
+ | eprintclass = {cs.CV}, | ||
+ | titleurl = {brahimi2019springer.pdf}, | ||
+ | } | ||
+ | |||
+ | @inproceedings{haefner20193dv, | ||
+ | title = {Photometric Segmentation: | ||
+ | author = {Haefner, B. and Quéau, Y. and Cremers, D.}, | ||
+ | booktitle={International Conference on 3D Vision (3DV)}, | ||
+ | month={September}, | ||
+ | address={Québec City, Canada}, | ||
+ | year = {2019}, | ||
+ | award = {Spotlight Presentation}, | ||
+ | titleurl = {haefner20193dv.pdf}, | ||
+ | note = { | ||
+ | {<a href="/ | ||
+ | }, | ||
} | } | ||
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note = { | note = { | ||
{<a href="/ | {<a href="/ | ||
+ | {<a href="/ | ||
{<a href=" | {<a href=" | ||
{<a href=" | {<a href=" | ||
Line 8201: | Line 8229: | ||
booktitle = ismrm, | booktitle = ismrm, | ||
keywords = {novelty detection, anomaly detection, machine learning, medical imaging, magnetic resonance imaging, diffusion MRI, segmentation}, | keywords = {novelty detection, anomaly detection, machine learning, medical imaging, magnetic resonance imaging, diffusion MRI, segmentation}, | ||
+ | } | ||
+ | |||
+ | @inproceedings{Vasilev-et-al-2018, | ||
+ | author = {A. Vasilev and V. Golkov and M. Meissner and I. Lipp and E. Sgarlata and V. Tomassini and D. K. Jones and D. Cremers}, | ||
+ | title = {{q}-{S}pace Novelty Detection with Variational Autoencoders}, | ||
+ | year = {2019}, | ||
+ | booktitle = {MICCAI 2019 International Workshop on Computational Diffusion MRI}, | ||
+ | eprint = {1806.02997}, | ||
+ | eprinttype = {arXiv}, | ||
+ | keywords = {deep learning, novelty detection, anomaly detection, neural networks, medical imaging, magnetic resonance imaging, diffusion MRI, deeplearning, | ||
+ | award = {Oral Presentation} | ||
} | } | ||
Line 8351: | Line 8390: | ||
} | } | ||
+ | @InProceedings{schubert2019vidsors, | ||
+ | author = "D. Schubert and N. Demmel and L. von Stumberg and V. Usenko and D. Cremers", | ||
+ | title = " | ||
+ | booktitle = iros, | ||
+ | year = " | ||
+ | month = " | ||
+ | arXiv = " | ||
+ | note = {{<a href=" | ||
+ | keywords = vidsors | ||
+ | } | ||
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year={2010}, | year={2010}, | ||
organization={IEEE} | organization={IEEE} | ||
- | } | ||
- | |||
- | @article{Vasilev-et-al-2018, | ||
- | author = {A. Vasilev and V. Golkov and M. Meissner and I. Lipp and E. Sgarlata and V. Tomassini and D. K. Jones and D. Cremers}, | ||
- | title = {{q}-{S}pace Novelty Detection with Variational Autoencoders}, | ||
- | year = {2018}, | ||
- | journal = {arXiv preprint arXiv: | ||
- | eprint = {1806.02997}, | ||
- | eprinttype = {arXiv}, | ||
- | keywords = {deep learning, novelty detection, anomaly detection, neural networks, medical imaging, magnetic resonance imaging, diffusion MRI, deeplearning, | ||
} | } | ||
Line 8393: | Line 8432: | ||
} | } | ||
- | @article{eisenberger2019divfree, | + | @InProceedings{eisenberger2019divfree, |
author = "M. Eisenberger and Z. L\" | author = "M. Eisenberger and Z. L\" | ||
title = " | title = " | ||
- | | + | |
arXiv = " arXiv: | arXiv = " arXiv: | ||
year = " | year = " | ||
month = " | month = " | ||
- | note = {Will be presented at Symposium on Geometry Processing (SGP) {<a href=" | + | note = {{<a href=" |
} | } | ||
Line 8457: | Line 8496: | ||
} | } | ||
- | @InProceedings{sundermeyer2018eccv, | + | @InProceedings{sundermeyer18implicit, |
author = {M. Sundermeyer and Z. Marton and M. Durner and M. Brucker and R. Triebel}, | author = {M. Sundermeyer and Z. Marton and M. Durner and M. Brucker and R. Triebel}, | ||
title = {Implicit 3D Orientation Learning for 6D Object Detection from RGB Images}, | title = {Implicit 3D Orientation Learning for 6D Object Detection from RGB Images}, | ||
Line 8466: | Line 8505: | ||
} | } | ||
- | @InProceedings{denninger18iros, | + | @InProceedings{denninger18persistent, |
author = {M. Denninger and R. Triebel}, | author = {M. Denninger and R. Triebel}, | ||
title = {Persistent Anytime Learning of Objects from Unseen Classes }, | title = {Persistent Anytime Learning of Objects from Unseen Classes }, | ||
Line 8475: | Line 8514: | ||
award = {Best Cognitive Robotics Paper Finalist}, | award = {Best Cognitive Robotics Paper Finalist}, | ||
} | } | ||
- | @InProceedings{grixa18iros, | + | @InProceedings{grixa18appearance, |
author = {I. Grixa and P. Schulz and W. St\" | author = {I. Grixa and P. Schulz and W. St\" | ||
title = {Appearance-Based Along-Route Localization for Planetary Missions}, | title = {Appearance-Based Along-Route Localization for Planetary Missions}, | ||
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award = {Oral Presentation}, | award = {Oral Presentation}, | ||
note = {{<a href=" | note = {{<a href=" | ||
- | } | ||
- | |||
- | @article{moeller-et-al-19, | ||
- | author = "M. Moeller and T. M{\" | ||
- | title = " | ||
- | journal = {preprint}, | ||
- | year = " | ||
- | note = {{<a href=" | ||
} | } | ||
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journal = {preprint}, | journal = {preprint}, | ||
year = " | year = " | ||
- | note = {{<a href=" | + | note = {{<a href=" |
- | keywords = {stereo, 3D reconstruction, | + | keywords = {stereo, 3D reconstruction, |
} | } | ||
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year = " | year = " | ||
note = {{<a href=" | note = {{<a href=" | ||
- | award = {Full Oral Presentation} | + | award = {Full Oral Presentation}, |
+ | month={6} | ||
} | } | ||
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@article{gn-net-19, | @article{gn-net-19, | ||
author = "L. von Stumberg and P. Wenzel and Q. Khan and D. Cremers", | author = "L. von Stumberg and P. Wenzel and Q. Khan and D. Cremers", | ||
- | title = " | + | title = " |
journal = {preprint}, | journal = {preprint}, | ||
year = " | year = " | ||
- | note = {{<a href=" | + | note = {{<a href=" |
keywords = {gn-net} | keywords = {gn-net} | ||
} | } | ||
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} | } | ||
- | @article{control-across-weathers-19, | + | @inproceedings{control-across-weathers-19, |
author = "Q. Khan and P. Wenzel and D. Cremers and L. Leal-Taixe", | author = "Q. Khan and P. Wenzel and D. Cremers and L. Leal-Taixe", | ||
title = " | title = " | ||
- | | + | |
year = " | year = " | ||
note = {{<a href=" | note = {{<a href=" | ||
} | } | ||
+ | |||
+ | @InProceedings{puang19visual, | ||
+ | title = {{Visual Repetition Sampling for Robot Manipulation Planning}}, | ||
+ | author = {E.Y. Puang and P. Lehner and Z.C. Marton and M. Durner and R. Triebel and A. Albu-Sch\" | ||
+ | booktitle = icra, | ||
+ | year = {2019} | ||
+ | } | ||
+ | |||
+ | @inproceedings{moeller-et-al-19, | ||
+ | author = "M. Moeller and T. M{\" | ||
+ | title = " | ||
+ | booktitle={International Conference on Computer Vision (ICCV)}, | ||
+ | year = " | ||
+ | month= {10}, | ||
+ | address={Seoul, | ||
+ | eprint = {1904.03081}, | ||
+ | eprinttype = {arXiv}, | ||
+ | eprintclass = {cs.CV}, | ||
+ | } | ||
+ | |||
+ | @inproceedings{jung2019corl, | ||
+ | author = {E. Jung and N. Yang and D. Cremers}, | ||
+ | booktitle = {Conference on Robot Learning (CoRL)}, | ||
+ | title = {{Multi-Frame GAN: Image Enhancement for Stereo Visual Odometry in Low Light}}, | ||
+ | award = {Full Oral Presentation}, | ||
+ | note = {{<a href=" | ||
+ | year = {2019} | ||
+ | } | ||
+ | |||
+ | @inproceedings{weiss2019sparse, | ||
+ | title={Sparse Surface Constraints for Combining Physics-based Elasticity Simulation and Correspondence-Free Object Reconstruction}, | ||
+ | author={S. Weiss and R. Maier and R. Westermann and D. Cremers and N. Thuerey}, | ||
+ | journal = {preprint}, | ||
+ | booktitle = {arXiv preprint arXiv: | ||
+ | year={2019}, | ||
+ | eprint = {1910.01812}, | ||
+ | eprinttype = {arXiv}, | ||
+ | eprintclass = {cs.CV}, | ||
+ | note = {{<a href=" | ||
+ | } | ||
+ | |||
+ | @article{Della-Libera-et-al-2019, | ||
+ | author = {L. Della Libera and V. Golkov and Y. Zhu and A. Mielke and D. Cremers}, | ||
+ | title = {Deep Learning for 2D and 3D Rotatable Data: An Overview of Methods}, | ||
+ | year = {2019}, | ||
+ | journal = {arXiv preprint arXiv: | ||
+ | eprint = {1910.14594}, | ||
+ | eprinttype = {arXiv}, | ||
+ | keywords = {deep learning, neural networks, 2D, 3D, rotations, invariance, equivariance, | ||
+ | } | ||
+ | |||
+ | @InBook{vi-dso-chapter, | ||
+ | author = {L. von Stumberg and V. Usenko and D. Cremers}, | ||
+ | editor = {Michael Ying Yang and Bodo Rosenhahn and Vittorio Murino}, | ||
+ | title = {A Review and Quantitative Evaluation of Direct Visual–Inertial Odometry}, | ||
+ | chapter = {Multimodal Scene Understanding}, | ||
+ | publisher = {Elsevier}, | ||
+ | year = {2019} | ||
+ | } | ||
+ | |||
+ | @InProceedings{sommer19spline, | ||
+ | author = "C. Sommer and V. Usenko and D. Schubert and N. Demmel and D. Cremers", | ||
+ | title = " | ||
+ | eprint = {1911.08860}, | ||
+ | eprinttype = {arXiv}, | ||
+ | eprintclass = {cs.CV}, | ||
+ | booktitle={arXiv: | ||
+ | year = " | ||
+ | } | ||
+ | |||
+ |