---
_id: '14448'
abstract:
- lang: eng
text: We consider the problem of solving LP relaxations of MAP-MRF inference problems,
and in particular the method proposed recently in [16], [35]. As a key computational
subroutine, it uses a variant of the Frank-Wolfe (FW) method to minimize a smooth
convex function over a combinatorial polytope. We propose an efficient implementation
of this subroutine based on in-face Frank-Wolfe directions, introduced in [4]
in a different context. More generally, we define an abstract data structure for
a combinatorial subproblem that enables in-face FW directions, and describe its
specialization for tree-structured MAP-MRF inference subproblems. Experimental
results indicate that the resulting method is the current state-of-art LP solver
for some classes of problems. Our code is available at pub.ist.ac.at/~vnk/papers/IN-FACE-FW.html.
article_processing_charge: No
arxiv: 1
author:
- first_name: Vladimir
full_name: Kolmogorov, Vladimir
id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87
last_name: Kolmogorov
citation:
ama: 'Kolmogorov V. Solving relaxations of MAP-MRF problems: Combinatorial in-face
Frank-Wolfe directions. In: Proceedings of the IEEE Computer Society Conference
on Computer Vision and Pattern Recognition. Vol 2023. IEEE; 2023:11980-11989.
doi:10.1109/CVPR52729.2023.01153'
apa: 'Kolmogorov, V. (2023). Solving relaxations of MAP-MRF problems: Combinatorial
in-face Frank-Wolfe directions. In Proceedings of the IEEE Computer Society
Conference on Computer Vision and Pattern Recognition (Vol. 2023, pp. 11980–11989).
Vancouver, Canada: IEEE. https://doi.org/10.1109/CVPR52729.2023.01153'
chicago: 'Kolmogorov, Vladimir. “Solving Relaxations of MAP-MRF Problems: Combinatorial
in-Face Frank-Wolfe Directions.” In Proceedings of the IEEE Computer Society
Conference on Computer Vision and Pattern Recognition, 2023:11980–89. IEEE,
2023. https://doi.org/10.1109/CVPR52729.2023.01153.'
ieee: 'V. Kolmogorov, “Solving relaxations of MAP-MRF problems: Combinatorial in-face
Frank-Wolfe directions,” in Proceedings of the IEEE Computer Society Conference
on Computer Vision and Pattern Recognition, Vancouver, Canada, 2023, vol.
2023, pp. 11980–11989.'
ista: 'Kolmogorov V. 2023. Solving relaxations of MAP-MRF problems: Combinatorial
in-face Frank-Wolfe directions. Proceedings of the IEEE Computer Society Conference
on Computer Vision and Pattern Recognition. CVPR: Conference on Computer Vision
and Pattern Recognition vol. 2023, 11980–11989.'
mla: 'Kolmogorov, Vladimir. “Solving Relaxations of MAP-MRF Problems: Combinatorial
in-Face Frank-Wolfe Directions.” Proceedings of the IEEE Computer Society Conference
on Computer Vision and Pattern Recognition, vol. 2023, IEEE, 2023, pp. 11980–89,
doi:10.1109/CVPR52729.2023.01153.'
short: V. Kolmogorov, in:, Proceedings of the IEEE Computer Society Conference on
Computer Vision and Pattern Recognition, IEEE, 2023, pp. 11980–11989.
conference:
end_date: 2023-06-24
location: Vancouver, Canada
name: 'CVPR: Conference on Computer Vision and Pattern Recognition'
start_date: 2023-06-17
corr_author: '1'
date_created: 2023-10-22T22:01:16Z
date_published: 2023-08-22T00:00:00Z
date_updated: 2025-09-09T13:09:58Z
day: '22'
department:
- _id: VlKo
doi: 10.1109/CVPR52729.2023.01153
external_id:
arxiv:
- '2010.09567'
isi:
- '001062522104029'
intvolume: ' 2023'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.2010.09567'
month: '08'
oa: 1
oa_version: Preprint
page: 11980-11989
publication: Proceedings of the IEEE Computer Society Conference on Computer Vision
and Pattern Recognition
publication_identifier:
isbn:
- '9798350301298'
issn:
- 1063-6919
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Solving relaxations of MAP-MRF problems: Combinatorial in-face Frank-Wolfe
directions'
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 2023
year: '2023'
...
---
_id: '14114'
abstract:
- lang: eng
text: Algorithmic fairness is frequently motivated in terms of a trade-off in which
overall performance is decreased so as to improve performance on disadvantaged
groups where the algorithm would otherwise be less accurate. Contrary to this,
we find that applying existing fairness approaches to computer vision improve
fairness by degrading the performance of classifiers across all groups (with increased
degradation on the best performing groups). Extending the bias-variance decomposition
for classification to fairness, we theoretically explain why the majority of fairness
methods designed for low capacity models should not be used in settings involving
high-capacity models, a scenario common to computer vision. We corroborate this
analysis with extensive experimental support that shows that many of the fairness
heuristics used in computer vision also degrade performance on the most disadvantaged
groups. Building on these insights, we propose an adaptive augmentation strategy
that, uniquely, of all methods tested, improves performance for the disadvantaged
groups.
article_processing_charge: No
arxiv: 1
author:
- first_name: Dominik
full_name: Zietlow, Dominik
last_name: Zietlow
- first_name: Michael
full_name: Lohaus, Michael
last_name: Lohaus
- first_name: Guha
full_name: Balakrishnan, Guha
last_name: Balakrishnan
- first_name: Matthaus
full_name: Kleindessner, Matthaus
last_name: Kleindessner
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Bernhard
full_name: Scholkopf, Bernhard
last_name: Scholkopf
- first_name: Chris
full_name: Russell, Chris
last_name: Russell
citation:
ama: 'Zietlow D, Lohaus M, Balakrishnan G, et al. Leveling down in computer vision:
Pareto inefficiencies in fair deep classifiers. In: 2022 IEEE/CVF Conference
on Computer Vision and Pattern Recognition. Institute of Electrical and Electronics
Engineers; 2022:10400-10411. doi:10.1109/cvpr52688.2022.01016'
apa: 'Zietlow, D., Lohaus, M., Balakrishnan, G., Kleindessner, M., Locatello, F.,
Scholkopf, B., & Russell, C. (2022). Leveling down in computer vision: Pareto
inefficiencies in fair deep classifiers. In 2022 IEEE/CVF Conference on Computer
Vision and Pattern Recognition (pp. 10400–10411). New Orleans, LA, United
States: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/cvpr52688.2022.01016'
chicago: 'Zietlow, Dominik, Michael Lohaus, Guha Balakrishnan, Matthaus Kleindessner,
Francesco Locatello, Bernhard Scholkopf, and Chris Russell. “Leveling down in
Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers.” In 2022 IEEE/CVF
Conference on Computer Vision and Pattern Recognition, 10400–411. Institute
of Electrical and Electronics Engineers, 2022. https://doi.org/10.1109/cvpr52688.2022.01016.'
ieee: 'D. Zietlow et al., “Leveling down in computer vision: Pareto inefficiencies
in fair deep classifiers,” in 2022 IEEE/CVF Conference on Computer Vision and
Pattern Recognition, New Orleans, LA, United States, 2022, pp. 10400–10411.'
ista: 'Zietlow D, Lohaus M, Balakrishnan G, Kleindessner M, Locatello F, Scholkopf
B, Russell C. 2022. Leveling down in computer vision: Pareto inefficiencies in
fair deep classifiers. 2022 IEEE/CVF Conference on Computer Vision and Pattern
Recognition. CVPR: Conference on Computer Vision and Pattern Recognition, 10400–10411.'
mla: 'Zietlow, Dominik, et al. “Leveling down in Computer Vision: Pareto Inefficiencies
in Fair Deep Classifiers.” 2022 IEEE/CVF Conference on Computer Vision and
Pattern Recognition, Institute of Electrical and Electronics Engineers, 2022,
pp. 10400–11, doi:10.1109/cvpr52688.2022.01016.'
short: D. Zietlow, M. Lohaus, G. Balakrishnan, M. Kleindessner, F. Locatello, B.
Scholkopf, C. Russell, in:, 2022 IEEE/CVF Conference on Computer Vision and Pattern
Recognition, Institute of Electrical and Electronics Engineers, 2022, pp. 10400–10411.
conference:
end_date: 2022-06-24
location: New Orleans, LA, United States
name: 'CVPR: Conference on Computer Vision and Pattern Recognition'
start_date: 2022-06-18
date_created: 2023-08-21T12:18:00Z
date_published: 2022-07-01T00:00:00Z
date_updated: 2023-09-11T09:19:14Z
day: '01'
department:
- _id: FrLo
doi: 10.1109/cvpr52688.2022.01016
extern: '1'
external_id:
arxiv:
- '2203.04913'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2203.04913
month: '07'
oa: 1
oa_version: Preprint
page: 10400-10411
publication: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition
publication_identifier:
eissn:
- 2575-7075
isbn:
- '9781665469470'
issn:
- 1063-6919
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '9957'
abstract:
- lang: eng
text: The reflectance field of a face describes the reflectance properties responsible
for complex lighting effects including diffuse, specular, inter-reflection and
self shadowing. Most existing methods for estimating the face reflectance from
a monocular image assume faces to be diffuse with very few approaches adding a
specular component. This still leaves out important perceptual aspects of reflectance
as higher-order global illumination effects and self-shadowing are not modeled.
We present a new neural representation for face reflectance where we can estimate
all components of the reflectance responsible for the final appearance from a
single monocular image. Instead of modeling each component of the reflectance
separately using parametric models, our neural representation allows us to generate
a basis set of faces in a geometric deformation-invariant space, parameterized
by the input light direction, viewpoint and face geometry. We learn to reconstruct
this reflectance field of a face just from a monocular image, which can be used
to render the face from any viewpoint in any light condition. Our method is trained
on a light-stage training dataset, which captures 300 people illuminated with
150 light conditions from 8 viewpoints. We show that our method outperforms existing
monocular reflectance reconstruction methods, in terms of photorealism due to
better capturing of physical premitives, such as sub-surface scattering, specularities,
self-shadows and other higher-order effects.
acknowledgement: "We thank Tarun Yenamandra and Duarte David for helping us with the
comparisons. This work was supported by the\r\nERC Consolidator Grant 4DReply (770784).
We also acknowledge support from InterDigital."
article_processing_charge: No
arxiv: 1
author:
- first_name: Mallikarjun
full_name: B R, Mallikarjun
last_name: B R
- first_name: Ayush
full_name: Tewari, Ayush
last_name: Tewari
- first_name: Tae-Hyun
full_name: Oh, Tae-Hyun
last_name: Oh
- first_name: Tim
full_name: Weyrich, Tim
last_name: Weyrich
- first_name: Bernd
full_name: Bickel, Bernd
id: 49876194-F248-11E8-B48F-1D18A9856A87
last_name: Bickel
orcid: 0000-0001-6511-9385
- first_name: Hans-Peter
full_name: Seidel, Hans-Peter
last_name: Seidel
- first_name: Hanspeter
full_name: Pfister, Hanspeter
last_name: Pfister
- first_name: Wojciech
full_name: Matusik, Wojciech
last_name: Matusik
- first_name: Mohamed
full_name: Elgharib, Mohamed
last_name: Elgharib
- first_name: Christian
full_name: Theobalt, Christian
last_name: Theobalt
citation:
ama: 'B R M, Tewari A, Oh T-H, et al. Monocular reconstruction of neural face reflectance
fields. In: Proceedings of the IEEE Computer Society Conference on Computer
Vision and Pattern Recognition. IEEE; 2021:4791-4800. doi:10.1109/CVPR46437.2021.00476'
apa: 'B R, M., Tewari, A., Oh, T.-H., Weyrich, T., Bickel, B., Seidel, H.-P., …
Theobalt, C. (2021). Monocular reconstruction of neural face reflectance fields.
In Proceedings of the IEEE Computer Society Conference on Computer Vision and
Pattern Recognition (pp. 4791–4800). Nashville, TN, United States; Virtual:
IEEE. https://doi.org/10.1109/CVPR46437.2021.00476'
chicago: B R, Mallikarjun, Ayush Tewari, Tae-Hyun Oh, Tim Weyrich, Bernd Bickel,
Hans-Peter Seidel, Hanspeter Pfister, Wojciech Matusik, Mohamed Elgharib, and
Christian Theobalt. “Monocular Reconstruction of Neural Face Reflectance Fields.”
In Proceedings of the IEEE Computer Society Conference on Computer Vision and
Pattern Recognition, 4791–4800. IEEE, 2021. https://doi.org/10.1109/CVPR46437.2021.00476.
ieee: M. B R et al., “Monocular reconstruction of neural face reflectance
fields,” in Proceedings of the IEEE Computer Society Conference on Computer
Vision and Pattern Recognition, Nashville, TN, United States; Virtual, 2021,
pp. 4791–4800.
ista: 'B R M, Tewari A, Oh T-H, Weyrich T, Bickel B, Seidel H-P, Pfister H, Matusik
W, Elgharib M, Theobalt C. 2021. Monocular reconstruction of neural face reflectance
fields. Proceedings of the IEEE Computer Society Conference on Computer Vision
and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition,
4791–4800.'
mla: B R, Mallikarjun, et al. “Monocular Reconstruction of Neural Face Reflectance
Fields.” Proceedings of the IEEE Computer Society Conference on Computer Vision
and Pattern Recognition, IEEE, 2021, pp. 4791–800, doi:10.1109/CVPR46437.2021.00476.
short: M. B R, A. Tewari, T.-H. Oh, T. Weyrich, B. Bickel, H.-P. Seidel, H. Pfister,
W. Matusik, M. Elgharib, C. Theobalt, in:, Proceedings of the IEEE Computer Society
Conference on Computer Vision and Pattern Recognition, IEEE, 2021, pp. 4791–4800.
conference:
end_date: 2021-06-25
location: Nashville, TN, United States; Virtual
name: 'CVPR: Conference on Computer Vision and Pattern Recognition'
start_date: 2021-06-20
date_created: 2021-08-24T06:03:00Z
date_published: 2021-09-01T00:00:00Z
date_updated: 2023-08-11T11:08:35Z
day: '01'
ddc:
- '000'
department:
- _id: BeBi
doi: 10.1109/CVPR46437.2021.00476
external_id:
arxiv:
- '2008.10247'
isi:
- '000739917304096'
file:
- access_level: open_access
checksum: 961db0bde76dd87cf833930080bb9f38
content_type: application/pdf
creator: bbickel
date_created: 2021-08-24T06:02:15Z
date_updated: 2021-08-24T06:02:15Z
file_id: '9958'
file_name: R_Monocular_Reconstruction_of_Neural_Face_Reflectance_Fields_CVPR_2021_paper[1].pdf
file_size: 4746649
relation: main_file
file_date_updated: 2021-08-24T06:02:15Z
has_accepted_license: '1'
isi: 1
language:
- iso: eng
month: '09'
oa: 1
oa_version: Preprint
page: 4791-4800
publication: Proceedings of the IEEE Computer Society Conference on Computer Vision
and Pattern Recognition
publication_identifier:
isbn:
- 978-166544509-2
issn:
- 1063-6919
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Monocular reconstruction of neural face reflectance fields
type: conference
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
year: '2021'
...
---
_id: '7468'
abstract:
- lang: eng
text: We present a new proximal bundle method for Maximum-A-Posteriori (MAP) inference
in structured energy minimization problems. The method optimizes a Lagrangean
relaxation of the original energy minimization problem using a multi plane block-coordinate
Frank-Wolfe method that takes advantage of the specific structure of the Lagrangean
decomposition. We show empirically that our method outperforms state-of-the-art
Lagrangean decomposition based algorithms on some challenging Markov Random Field,
multi-label discrete tomography and graph matching problems.
article_number: 11138-11147
article_processing_charge: No
arxiv: 1
author:
- first_name: Paul
full_name: Swoboda, Paul
id: 446560C6-F248-11E8-B48F-1D18A9856A87
last_name: Swoboda
- first_name: Vladimir
full_name: Kolmogorov, Vladimir
id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87
last_name: Kolmogorov
citation:
ama: 'Swoboda P, Kolmogorov V. Map inference via block-coordinate Frank-Wolfe algorithm.
In: Proceedings of the IEEE Computer Society Conference on Computer Vision
and Pattern Recognition. Vol 2019-June. IEEE; 2019. doi:10.1109/CVPR.2019.01140'
apa: 'Swoboda, P., & Kolmogorov, V. (2019). Map inference via block-coordinate
Frank-Wolfe algorithm. In Proceedings of the IEEE Computer Society Conference
on Computer Vision and Pattern Recognition (Vol. 2019–June). Long Beach, CA,
United States: IEEE. https://doi.org/10.1109/CVPR.2019.01140'
chicago: Swoboda, Paul, and Vladimir Kolmogorov. “Map Inference via Block-Coordinate
Frank-Wolfe Algorithm.” In Proceedings of the IEEE Computer Society Conference
on Computer Vision and Pattern Recognition, Vol. 2019–June. IEEE, 2019. https://doi.org/10.1109/CVPR.2019.01140.
ieee: P. Swoboda and V. Kolmogorov, “Map inference via block-coordinate Frank-Wolfe
algorithm,” in Proceedings of the IEEE Computer Society Conference on Computer
Vision and Pattern Recognition, Long Beach, CA, United States, 2019, vol.
2019–June.
ista: 'Swoboda P, Kolmogorov V. 2019. Map inference via block-coordinate Frank-Wolfe
algorithm. Proceedings of the IEEE Computer Society Conference on Computer Vision
and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition
vol. 2019–June, 11138–11147.'
mla: Swoboda, Paul, and Vladimir Kolmogorov. “Map Inference via Block-Coordinate
Frank-Wolfe Algorithm.” Proceedings of the IEEE Computer Society Conference
on Computer Vision and Pattern Recognition, vol. 2019–June, 11138–11147, IEEE,
2019, doi:10.1109/CVPR.2019.01140.
short: P. Swoboda, V. Kolmogorov, in:, Proceedings of the IEEE Computer Society
Conference on Computer Vision and Pattern Recognition, IEEE, 2019.
conference:
end_date: 2019-06-20
location: Long Beach, CA, United States
name: 'CVPR: Conference on Computer Vision and Pattern Recognition'
start_date: 2019-06-15
date_created: 2020-02-09T23:00:52Z
date_published: 2019-06-01T00:00:00Z
date_updated: 2025-07-10T11:54:39Z
day: '01'
department:
- _id: VlKo
doi: 10.1109/CVPR.2019.01140
ec_funded: 1
external_id:
arxiv:
- '1806.05049'
isi:
- '000542649304076'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1806.05049
month: '06'
oa: 1
oa_version: Preprint
project:
- _id: 25FBA906-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '616160'
name: 'Discrete Optimization in Computer Vision: Theory and Practice'
publication: Proceedings of the IEEE Computer Society Conference on Computer Vision
and Pattern Recognition
publication_identifier:
isbn:
- '9781728132938'
issn:
- 1063-6919
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Map inference via block-coordinate Frank-Wolfe algorithm
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 2019-June
year: '2019'
...
---
_id: '18287'
abstract:
- lang: eng
text: Many algorithms for the computation of correspondences between deformable
shapes rely on some variant of nearest neighbor matching in a descriptor space.
Such are, for example, various point-wise correspondence recovery algorithms used
as a post-processing stage in the functional correspondence framework. Such frequently
used techniques implicitly make restrictive assumptions (e.g., nearisometry) on
the considered shapes and in practice suffer from lack of accuracy and result
in poor surjectivity. We propose an alternative recovery technique capable of
guaranteeing a bijective correspondence and producing significantly higher accuracy
and smoothness. Unlike other methods our approach does not depend on the assumption
that the analyzed shapes are isometric. We derive the proposed method from the
statistical framework of kernel density estimation and demonstrate its performance
on several challenging deformable 3D shape matching datasets.
article_processing_charge: No
arxiv: 1
author:
- first_name: Matthias
full_name: Vestner, Matthias
last_name: Vestner
- first_name: Roee
full_name: Litman, Roee
last_name: Litman
- first_name: Emanuele
full_name: Rodola, Emanuele
last_name: Rodola
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Daniel
full_name: Cremers, Daniel
last_name: Cremers
citation:
ama: 'Vestner M, Litman R, Rodola E, Bronstein AM, Cremers D. Product manifold filter:
Non-rigid shape correspondence via kernel density estimation in the product space.
In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
IEEE; 2017:6681-6690. doi:10.1109/cvpr.2017.707'
apa: 'Vestner, M., Litman, R., Rodola, E., Bronstein, A. M., & Cremers, D. (2017).
Product manifold filter: Non-rigid shape correspondence via kernel density estimation
in the product space. In 2017 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) (pp. 6681–6690). Honolulu, HI, United States: IEEE. https://doi.org/10.1109/cvpr.2017.707'
chicago: 'Vestner, Matthias, Roee Litman, Emanuele Rodola, Alex M. Bronstein, and
Daniel Cremers. “Product Manifold Filter: Non-Rigid Shape Correspondence via Kernel
Density Estimation in the Product Space.” In 2017 IEEE Conference on Computer
Vision and Pattern Recognition (CVPR), 6681–90. IEEE, 2017. https://doi.org/10.1109/cvpr.2017.707.'
ieee: 'M. Vestner, R. Litman, E. Rodola, A. M. Bronstein, and D. Cremers, “Product
manifold filter: Non-rigid shape correspondence via kernel density estimation
in the product space,” in 2017 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), Honolulu, HI, United States, 2017, pp. 6681–6690.'
ista: 'Vestner M, Litman R, Rodola E, Bronstein AM, Cremers D. 2017. Product manifold
filter: Non-rigid shape correspondence via kernel density estimation in the product
space. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
30th IEEE Conference on Computer Vision and Pattern Recognition, 6681–6690.'
mla: 'Vestner, Matthias, et al. “Product Manifold Filter: Non-Rigid Shape Correspondence
via Kernel Density Estimation in the Product Space.” 2017 IEEE Conference on
Computer Vision and Pattern Recognition (CVPR), IEEE, 2017, pp. 6681–90, doi:10.1109/cvpr.2017.707.'
short: M. Vestner, R. Litman, E. Rodola, A.M. Bronstein, D. Cremers, in:, 2017 IEEE
Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2017, pp.
6681–6690.
conference:
end_date: 2017-07-26
location: Honolulu, HI, United States
name: 30th IEEE Conference on Computer Vision and Pattern Recognition
start_date: 2017-07-21
date_created: 2024-10-09T07:49:43Z
date_published: 2017-11-09T00:00:00Z
date_updated: 2024-12-05T14:20:16Z
day: '09'
doi: 10.1109/cvpr.2017.707
extern: '1'
external_id:
arxiv:
- '1701.00669'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.1701.00669
month: '11'
oa: 1
oa_version: Preprint
page: 6681 - 6690
publication: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
publication_identifier:
isbn:
- '9781538604588'
issn:
- 1063-6919
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Product manifold filter: Non-rigid shape correspondence via kernel density
estimation in the product space'
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2017'
...