---
_id: '14488'
abstract:
- lang: eng
text: 'Portrait viewpoint and illumination editing is an important problem with
several applications in VR/AR, movies, and photography. Comprehensive knowledge
of geometry and illumination is critical for obtaining photorealistic results.
Current methods are unable to explicitly model in 3D while handling both viewpoint
and illumination editing from a single image. In this paper, we propose VoRF,
a novel approach that can take even a single portrait image as input and relight
human heads under novel illuminations that can be viewed from arbitrary viewpoints.
VoRF represents a human head as a continuous volumetric field and learns a prior
model of human heads using a coordinate-based MLP with individual latent spaces
for identity and illumination. The prior model is learned in an auto-decoder manner
over a diverse class of head shapes and appearances, allowing VoRF to generalize
to novel test identities from a single input image. Additionally, VoRF has a reflectance
MLP that uses the intermediate features of the prior model for rendering One-Light-at-A-Time
(OLAT) images under novel views. We synthesize novel illuminations by combining
these OLAT images with target environment maps. Qualitative and quantitative evaluations
demonstrate the effectiveness of VoRF for relighting and novel view synthesis,
even when applied to unseen subjects under uncontrolled illumination. This work
is an extension of Rao et al. (VoRF: Volumetric Relightable Faces 2022). We provide
extensive evaluation and ablative studies of our model and also provide an application,
where any face can be relighted using textual input.'
acknowledgement: Open Access funding enabled and organized by Projekt DEAL.
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Pramod
full_name: Rao, Pramod
last_name: Rao
- first_name: B. R.
full_name: Mallikarjun, B. R.
last_name: Mallikarjun
- first_name: Gereon
full_name: Fox, Gereon
last_name: Fox
- 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: Hanspeter
full_name: Pfister, Hanspeter
last_name: Pfister
- first_name: Wojciech
full_name: Matusik, Wojciech
last_name: Matusik
- first_name: Fangneng
full_name: Zhan, Fangneng
last_name: Zhan
- first_name: Ayush
full_name: Tewari, Ayush
last_name: Tewari
- first_name: Christian
full_name: Theobalt, Christian
last_name: Theobalt
- first_name: Mohamed
full_name: Elgharib, Mohamed
last_name: Elgharib
citation:
ama: Rao P, Mallikarjun BR, Fox G, et al. A deeper analysis of volumetric relightiable
faces. International Journal of Computer Vision. 2024;132:1148-1166. doi:10.1007/s11263-023-01899-3
apa: Rao, P., Mallikarjun, B. R., Fox, G., Weyrich, T., Bickel, B., Pfister, H.,
… Elgharib, M. (2024). A deeper analysis of volumetric relightiable faces. International
Journal of Computer Vision. Springer Nature. https://doi.org/10.1007/s11263-023-01899-3
chicago: Rao, Pramod, B. R. Mallikarjun, Gereon Fox, Tim Weyrich, Bernd Bickel,
Hanspeter Pfister, Wojciech Matusik, et al. “A Deeper Analysis of Volumetric Relightiable
Faces.” International Journal of Computer Vision. Springer Nature, 2024.
https://doi.org/10.1007/s11263-023-01899-3.
ieee: P. Rao et al., “A deeper analysis of volumetric relightiable faces,”
International Journal of Computer Vision, vol. 132. Springer Nature, pp.
1148–1166, 2024.
ista: Rao P, Mallikarjun BR, Fox G, Weyrich T, Bickel B, Pfister H, Matusik W, Zhan
F, Tewari A, Theobalt C, Elgharib M. 2024. A deeper analysis of volumetric relightiable
faces. International Journal of Computer Vision. 132, 1148–1166.
mla: Rao, Pramod, et al. “A Deeper Analysis of Volumetric Relightiable Faces.” International
Journal of Computer Vision, vol. 132, Springer Nature, 2024, pp. 1148–66,
doi:10.1007/s11263-023-01899-3.
short: P. Rao, B.R. Mallikarjun, G. Fox, T. Weyrich, B. Bickel, H. Pfister, W. Matusik,
F. Zhan, A. Tewari, C. Theobalt, M. Elgharib, International Journal of Computer
Vision 132 (2024) 1148–1166.
date_created: 2023-11-05T23:00:54Z
date_published: 2024-04-01T00:00:00Z
date_updated: 2025-08-05T13:28:58Z
day: '01'
ddc:
- '000'
department:
- _id: BeBi
doi: 10.1007/s11263-023-01899-3
external_id:
isi:
- '001091935600002'
pmid:
- '38549787'
file:
- access_level: open_access
checksum: 5eef1d920f6fe700d7856098000d05f1
content_type: application/pdf
creator: dernst
date_created: 2024-07-22T11:07:14Z
date_updated: 2024-07-22T11:07:14Z
file_id: '17304'
file_name: 2024_IJCV_Rao.pdf
file_size: 9942520
relation: main_file
success: 1
file_date_updated: 2024-07-22T11:07:14Z
has_accepted_license: '1'
intvolume: ' 132'
isi: 1
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
page: 1148-1166
pmid: 1
publication: International Journal of Computer Vision
publication_identifier:
eissn:
- 1573-1405
issn:
- 0920-5691
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: A deeper analysis of volumetric relightiable faces
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 132
year: '2024'
...
---
_id: '6944'
abstract:
- lang: eng
text: 'We study the problem of automatically detecting if a given multi-class classifier
operates outside of its specifications (out-of-specs), i.e. on input data from
a different distribution than what it was trained for. This is an important problem
to solve on the road towards creating reliable computer vision systems for real-world
applications, because the quality of a classifier’s predictions cannot be guaranteed
if it operates out-of-specs. Previously proposed methods for out-of-specs detection
make decisions on the level of single inputs. This, however, is insufficient to
achieve low false positive rate and high false negative rates at the same time.
In this work, we describe a new procedure named KS(conf), based on statistical
reasoning. Its main component is a classical Kolmogorov–Smirnov test that is applied
to the set of predicted confidence values for batches of samples. Working with
batches instead of single samples allows increasing the true positive rate without
negatively affecting the false positive rate, thereby overcoming a crucial limitation
of single sample tests. We show by extensive experiments using a variety of convolutional
network architectures and datasets that KS(conf) reliably detects out-of-specs
situations even under conditions where other tests fail. It furthermore has a
number of properties that make it an excellent candidate for practical deployment:
it is easy to implement, adds almost no overhead to the system, works with any
classifier that outputs confidence scores, and requires no a priori knowledge
about how the data distribution could change.'
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Rémy
full_name: Sun, Rémy
last_name: Sun
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Sun R, Lampert C. KS(conf): A light-weight test if a multiclass classifier
operates outside of its specifications. International Journal of Computer Vision.
2020;128(4):970-995. doi:10.1007/s11263-019-01232-x'
apa: 'Sun, R., & Lampert, C. (2020). KS(conf): A light-weight test if a multiclass
classifier operates outside of its specifications. International Journal of
Computer Vision. Springer Nature. https://doi.org/10.1007/s11263-019-01232-x'
chicago: 'Sun, Rémy, and Christoph Lampert. “KS(Conf): A Light-Weight Test If a
Multiclass Classifier Operates Outside of Its Specifications.” International
Journal of Computer Vision. Springer Nature, 2020. https://doi.org/10.1007/s11263-019-01232-x.'
ieee: 'R. Sun and C. Lampert, “KS(conf): A light-weight test if a multiclass classifier
operates outside of its specifications,” International Journal of Computer
Vision, vol. 128, no. 4. Springer Nature, pp. 970–995, 2020.'
ista: 'Sun R, Lampert C. 2020. KS(conf): A light-weight test if a multiclass classifier
operates outside of its specifications. International Journal of Computer Vision.
128(4), 970–995.'
mla: 'Sun, Rémy, and Christoph Lampert. “KS(Conf): A Light-Weight Test If a Multiclass
Classifier Operates Outside of Its Specifications.” International Journal of
Computer Vision, vol. 128, no. 4, Springer Nature, 2020, pp. 970–95, doi:10.1007/s11263-019-01232-x.'
short: R. Sun, C. Lampert, International Journal of Computer Vision 128 (2020) 970–995.
corr_author: '1'
date_created: 2019-10-14T09:14:28Z
date_published: 2020-04-01T00:00:00Z
date_updated: 2025-04-15T07:10:25Z
day: '01'
ddc:
- '004'
department:
- _id: ChLa
doi: 10.1007/s11263-019-01232-x
ec_funded: 1
external_id:
isi:
- '000494406800001'
file:
- access_level: open_access
checksum: 155e63edf664dcacb3bdc1c2223e606f
content_type: application/pdf
creator: dernst
date_created: 2019-11-26T10:30:02Z
date_updated: 2020-07-14T12:47:45Z
file_id: '7110'
file_name: 2019_IJCV_Sun.pdf
file_size: 1715072
relation: main_file
file_date_updated: 2020-07-14T12:47:45Z
has_accepted_license: '1'
intvolume: ' 128'
isi: 1
issue: '4'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
page: 970-995
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
- _id: B67AFEDC-15C9-11EA-A837-991A96BB2854
name: IST Austria Open Access Fund
publication: International Journal of Computer Vision
publication_identifier:
eissn:
- 1573-1405
issn:
- 0920-5691
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
link:
- relation: erratum
url: https://doi.org/10.1007/s11263-019-01262-5
record:
- id: '6482'
relation: earlier_version
status: public
scopus_import: '1'
status: public
title: 'KS(conf): A light-weight test if a multiclass classifier operates outside
of its specifications'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 128
year: '2020'
...
---
_id: '6952'
abstract:
- lang: eng
text: 'We present a unified framework tackling two problems: class-specific 3D reconstruction
from a single image, and generation of new 3D shape samples. These tasks have
received considerable attention recently; however, most existing approaches rely
on 3D supervision, annotation of 2D images with keypoints or poses, and/or training
with multiple views of each object instance. Our framework is very general: it
can be trained in similar settings to existing approaches, while also supporting
weaker supervision. Importantly, it can be trained purely from 2D images, without
pose annotations, and with only a single view per instance. We employ meshes as
an output representation, instead of voxels used in most prior work. This allows
us to reason over lighting parameters and exploit shading information during training,
which previous 2D-supervised methods cannot. Thus, our method can learn to generate
and reconstruct concave object classes. We evaluate our approach in various settings,
showing that: (i) it learns to disentangle shape from pose and lighting; (ii)
using shading in the loss improves performance compared to just silhouettes; (iii)
when using a standard single white light, our model outperforms state-of-the-art
2D-supervised methods, both with and without pose supervision, thanks to exploiting
shading cues; (iv) performance improves further when using multiple coloured lights,
even approaching that of state-of-the-art 3D-supervised methods; (v) shapes produced
by our model capture smooth surfaces and fine details better than voxel-based
approaches; and (vi) our approach supports concave classes such as bathtubs and
sofas, which methods based on silhouettes cannot learn.'
acknowledgement: Open access funding provided by Institute of Science and Technology
(IST Austria).
article_processing_charge: Yes (via OA deal)
article_type: original
arxiv: 1
author:
- first_name: Paul M
full_name: Henderson, Paul M
id: 13C09E74-18D9-11E9-8878-32CFE5697425
last_name: Henderson
orcid: 0000-0002-5198-7445
- first_name: Vittorio
full_name: Ferrari, Vittorio
last_name: Ferrari
citation:
ama: Henderson PM, Ferrari V. Learning single-image 3D reconstruction by generative
modelling of shape, pose and shading. International Journal of Computer Vision.
2020;128:835-854. doi:10.1007/s11263-019-01219-8
apa: Henderson, P. M., & Ferrari, V. (2020). Learning single-image 3D reconstruction
by generative modelling of shape, pose and shading. International Journal of
Computer Vision. Springer Nature. https://doi.org/10.1007/s11263-019-01219-8
chicago: Henderson, Paul M, and Vittorio Ferrari. “Learning Single-Image 3D Reconstruction
by Generative Modelling of Shape, Pose and Shading.” International Journal
of Computer Vision. Springer Nature, 2020. https://doi.org/10.1007/s11263-019-01219-8.
ieee: P. M. Henderson and V. Ferrari, “Learning single-image 3D reconstruction by
generative modelling of shape, pose and shading,” International Journal of
Computer Vision, vol. 128. Springer Nature, pp. 835–854, 2020.
ista: Henderson PM, Ferrari V. 2020. Learning single-image 3D reconstruction by
generative modelling of shape, pose and shading. International Journal of Computer
Vision. 128, 835–854.
mla: Henderson, Paul M., and Vittorio Ferrari. “Learning Single-Image 3D Reconstruction
by Generative Modelling of Shape, Pose and Shading.” International Journal
of Computer Vision, vol. 128, Springer Nature, 2020, pp. 835–54, doi:10.1007/s11263-019-01219-8.
short: P.M. Henderson, V. Ferrari, International Journal of Computer Vision 128
(2020) 835–854.
corr_author: '1'
date_created: 2019-10-17T13:38:20Z
date_published: 2020-04-01T00:00:00Z
date_updated: 2025-04-15T06:53:15Z
day: '01'
ddc:
- '004'
department:
- _id: ChLa
doi: 10.1007/s11263-019-01219-8
external_id:
arxiv:
- '1901.06447'
isi:
- '000491042100002'
file:
- access_level: open_access
checksum: a0f05dd4f5f64e4f713d8d9d4b5b1e3f
content_type: application/pdf
creator: dernst
date_created: 2019-10-25T10:28:29Z
date_updated: 2020-07-14T12:47:46Z
file_id: '6973'
file_name: 2019_CompVision_Henderson.pdf
file_size: 2243134
relation: main_file
file_date_updated: 2020-07-14T12:47:46Z
has_accepted_license: '1'
intvolume: ' 128'
isi: 1
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
page: 835-854
project:
- _id: B67AFEDC-15C9-11EA-A837-991A96BB2854
name: IST Austria Open Access Fund
publication: International Journal of Computer Vision
publication_identifier:
eissn:
- 1573-1405
issn:
- 0920-5691
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Learning single-image 3D reconstruction by generative modelling of shape, pose
and shading
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 128
year: '2020'
...
---
_id: '18361'
abstract:
- lang: eng
text: 3D models of humans are commonly used within computer graphics and vision,
and so the ability to distinguish between body shapes is an important shape retrieval
problem. We extend our recent paper which provided a benchmark for testing non-rigid
3D shape retrieval algorithms on 3D human models. This benchmark provided a far
stricter challenge than previous shape benchmarks. We have added 145 new models
for use as a separate training set, in order to standardise the training data
used and provide a fairer comparison. We have also included experiments with the
FAUST dataset of human scans. All participants of the previous benchmark study
have taken part in the new tests reported here, many providing updated results
using the new data. In addition, further participants have also taken part, and
we provide extra analysis of the retrieval results. A total of 25 different shape
retrieval methods are compared.
article_processing_charge: No
author:
- first_name: D.
full_name: Pickup, D.
last_name: Pickup
- first_name: X.
full_name: Sun, X.
last_name: Sun
- first_name: P. L.
full_name: Rosin, P. L.
last_name: Rosin
- first_name: R. R.
full_name: Martin, R. R.
last_name: Martin
- first_name: Z.
full_name: Cheng, Z.
last_name: Cheng
- first_name: Z.
full_name: Lian, Z.
last_name: Lian
- first_name: M.
full_name: Aono, M.
last_name: Aono
- first_name: A. Ben
full_name: Hamza, A. Ben
last_name: Hamza
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: M.
full_name: Bronstein, M.
last_name: Bronstein
- first_name: S.
full_name: Bu, S.
last_name: Bu
- first_name: U.
full_name: Castellani, U.
last_name: Castellani
- first_name: S.
full_name: Cheng, S.
last_name: Cheng
- first_name: V.
full_name: Garro, V.
last_name: Garro
- first_name: A.
full_name: Giachetti, A.
last_name: Giachetti
- first_name: A.
full_name: Godil, A.
last_name: Godil
- first_name: L.
full_name: Isaia, L.
last_name: Isaia
- first_name: J.
full_name: Han, J.
last_name: Han
- first_name: H.
full_name: Johan, H.
last_name: Johan
- first_name: L.
full_name: Lai, L.
last_name: Lai
- first_name: B.
full_name: Li, B.
last_name: Li
- first_name: C.
full_name: Li, C.
last_name: Li
- first_name: H.
full_name: Li, H.
last_name: Li
- first_name: R.
full_name: Litman, R.
last_name: Litman
- first_name: X.
full_name: Liu, X.
last_name: Liu
- first_name: Z.
full_name: Liu, Z.
last_name: Liu
- first_name: Y.
full_name: Lu, Y.
last_name: Lu
- first_name: L.
full_name: Sun, L.
last_name: Sun
- first_name: G.
full_name: Tam, G.
last_name: Tam
- first_name: A.
full_name: Tatsuma, A.
last_name: Tatsuma
- first_name: J.
full_name: Ye, J.
last_name: Ye
citation:
ama: Pickup D, Sun X, Rosin PL, et al. Shape retrieval of non-rigid 3D human models.
International Journal of Computer Vision. 2016;120(2):169-193. doi:10.1007/s11263-016-0903-8
apa: Pickup, D., Sun, X., Rosin, P. L., Martin, R. R., Cheng, Z., Lian, Z., … Ye,
J. (2016). Shape retrieval of non-rigid 3D human models. International Journal
of Computer Vision. Springer Nature. https://doi.org/10.1007/s11263-016-0903-8
chicago: Pickup, D., X. Sun, P. L. Rosin, R. R. Martin, Z. Cheng, Z. Lian, M. Aono,
et al. “Shape Retrieval of Non-Rigid 3D Human Models.” International Journal
of Computer Vision. Springer Nature, 2016. https://doi.org/10.1007/s11263-016-0903-8.
ieee: D. Pickup et al., “Shape retrieval of non-rigid 3D human models,” International
Journal of Computer Vision, vol. 120, no. 2. Springer Nature, pp. 169–193,
2016.
ista: Pickup D, Sun X, Rosin PL, Martin RR, Cheng Z, Lian Z, Aono M, Hamza AB, Bronstein
AM, Bronstein M, Bu S, Castellani U, Cheng S, Garro V, Giachetti A, Godil A, Isaia
L, Han J, Johan H, Lai L, Li B, Li C, Li H, Litman R, Liu X, Liu Z, Lu Y, Sun
L, Tam G, Tatsuma A, Ye J. 2016. Shape retrieval of non-rigid 3D human models.
International Journal of Computer Vision. 120(2), 169–193.
mla: Pickup, D., et al. “Shape Retrieval of Non-Rigid 3D Human Models.” International
Journal of Computer Vision, vol. 120, no. 2, Springer Nature, 2016, pp. 169–93,
doi:10.1007/s11263-016-0903-8.
short: D. Pickup, X. Sun, P.L. Rosin, R.R. Martin, Z. Cheng, Z. Lian, M. Aono, A.B.
Hamza, A.M. Bronstein, M. Bronstein, S. Bu, U. Castellani, S. Cheng, V. Garro,
A. Giachetti, A. Godil, L. Isaia, J. Han, H. Johan, L. Lai, B. Li, C. Li, H. Li,
R. Litman, X. Liu, Z. Liu, Y. Lu, L. Sun, G. Tam, A. Tatsuma, J. Ye, International
Journal of Computer Vision 120 (2016) 169–193.
date_created: 2024-10-15T11:20:54Z
date_published: 2016-11-01T00:00:00Z
date_updated: 2024-12-18T15:13:03Z
day: '01'
doi: 10.1007/s11263-016-0903-8
extern: '1'
intvolume: ' 120'
issue: '2'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1007/s11263-016-0903-8
month: '11'
oa: 1
oa_version: Published Version
page: 169-193
publication: International Journal of Computer Vision
publication_identifier:
eissn:
- 1573-1405
issn:
- 0920-5691
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Shape retrieval of non-rigid 3D human models
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 120
year: '2016'
...
---
OA_type: closed access
_id: '18358'
abstract:
- lang: eng
text: In this paper, the problem of non-rigid shape recognition is studied from
the perspective of metric geometry. In particular, we explore the applicability
of diffusion distances within the Gromov-Hausdorff framework. While the traditionally
used geodesic distance exploits the shortest path between points on the surface,
the diffusion distance averages all paths connecting the points. The diffusion
distance constitutes an intrinsic metric which is robust, in particular, to topological
changes. Such changes in the form of shortcuts, holes, and missing data may be
a result of natural non-rigid deformations as well as acquisition and representation
noise due to inaccurate surface construction. The presentation of the proposed
framework is complemented with examples demonstrating that in addition to the
relatively low complexity involved in the computation of the diffusion distances
between surface points, its recognition and matching performances favorably compare
to the classical geodesic distances in the presence of topological changes between
the non-rigid shapes.
article_processing_charge: No
article_type: original
author:
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Michael M.
full_name: Bronstein, Michael M.
last_name: Bronstein
- first_name: Ron
full_name: Kimmel, Ron
last_name: Kimmel
- first_name: Mona
full_name: Mahmoudi, Mona
last_name: Mahmoudi
- first_name: Guillermo
full_name: Sapiro, Guillermo
last_name: Sapiro
citation:
ama: Bronstein AM, Bronstein MM, Kimmel R, Mahmoudi M, Sapiro G. A Gromov-Hausdorff
framework with diffusion geometry for topologically-robust non-rigid shape matching.
International Journal of Computer Vision. 2010;89(2-3):266-286. doi:10.1007/s11263-009-0301-6
apa: Bronstein, A. M., Bronstein, M. M., Kimmel, R., Mahmoudi, M., & Sapiro,
G. (2010). A Gromov-Hausdorff framework with diffusion geometry for topologically-robust
non-rigid shape matching. International Journal of Computer Vision. Springer
Nature. https://doi.org/10.1007/s11263-009-0301-6
chicago: Bronstein, Alex M., Michael M. Bronstein, Ron Kimmel, Mona Mahmoudi, and
Guillermo Sapiro. “A Gromov-Hausdorff Framework with Diffusion Geometry for Topologically-Robust
Non-Rigid Shape Matching.” International Journal of Computer Vision. Springer
Nature, 2010. https://doi.org/10.1007/s11263-009-0301-6.
ieee: A. M. Bronstein, M. M. Bronstein, R. Kimmel, M. Mahmoudi, and G. Sapiro, “A
Gromov-Hausdorff framework with diffusion geometry for topologically-robust non-rigid
shape matching,” International Journal of Computer Vision, vol. 89, no.
2–3. Springer Nature, pp. 266–286, 2010.
ista: Bronstein AM, Bronstein MM, Kimmel R, Mahmoudi M, Sapiro G. 2010. A Gromov-Hausdorff
framework with diffusion geometry for topologically-robust non-rigid shape matching.
International Journal of Computer Vision. 89(2–3), 266–286.
mla: Bronstein, Alex M., et al. “A Gromov-Hausdorff Framework with Diffusion Geometry
for Topologically-Robust Non-Rigid Shape Matching.” International Journal of
Computer Vision, vol. 89, no. 2–3, Springer Nature, 2010, pp. 266–86, doi:10.1007/s11263-009-0301-6.
short: A.M. Bronstein, M.M. Bronstein, R. Kimmel, M. Mahmoudi, G. Sapiro, International
Journal of Computer Vision 89 (2010) 266–286.
date_created: 2024-10-15T11:20:54Z
date_published: 2010-09-01T00:00:00Z
date_updated: 2024-10-22T07:58:23Z
day: '01'
doi: 10.1007/s11263-009-0301-6
extern: '1'
intvolume: ' 89'
issue: 2-3
language:
- iso: eng
month: '09'
oa_version: None
page: 266-286
publication: International Journal of Computer Vision
publication_identifier:
eissn:
- 1573-1405
issn:
- 0920-5691
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: A Gromov-Hausdorff framework with diffusion geometry for topologically-robust
non-rigid shape matching
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 89
year: '2010'
...
---
_id: '18359'
abstract:
- lang: eng
text: 'Symmetry and self-similarity are the cornerstone of Nature, exhibiting themselves
through the shapes of natural creations and ubiquitous laws of physics. Since
many natural objects are symmetric, the absence of symmetry can often be an indication
of some anomaly or abnormal behavior. Therefore, detection of asymmetries is important
in numerous practical applications, including crystallography, medical imaging,
and face recognition, to mention a few. Conversely, the assumption of underlying
shape symmetry can facilitate solutions to many problems in shape reconstruction
and analysis. Traditionally, symmetries are described as extrinsic geometric properties
of the shape. While being adequate for rigid shapes, such a description is inappropriate
for non-rigid ones: extrinsic symmetry can be broken as a result of shape deformations,
while its intrinsic symmetry is preserved. In this paper, we present a generalization
of symmetries for non-rigid shapes and a numerical framework for their analysis,
addressing the problems of full and partial exact and approximate symmetry detection
and classification.'
article_processing_charge: No
article_type: original
author:
- first_name: Dan
full_name: Raviv, Dan
last_name: Raviv
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Michael M.
full_name: Bronstein, Michael M.
last_name: Bronstein
- first_name: Ron
full_name: Kimmel, Ron
last_name: Kimmel
citation:
ama: Raviv D, Bronstein AM, Bronstein MM, Kimmel R. Full and partial symmetries
of non-rigid shapes. International Journal of Computer Vision. 2010;89:18-39.
doi:10.1007/s11263-010-0320-3
apa: Raviv, D., Bronstein, A. M., Bronstein, M. M., & Kimmel, R. (2010). Full
and partial symmetries of non-rigid shapes. International Journal of Computer
Vision. Springer Nature. https://doi.org/10.1007/s11263-010-0320-3
chicago: Raviv, Dan, Alex M. Bronstein, Michael M. Bronstein, and Ron Kimmel. “Full
and Partial Symmetries of Non-Rigid Shapes.” International Journal of Computer
Vision. Springer Nature, 2010. https://doi.org/10.1007/s11263-010-0320-3.
ieee: D. Raviv, A. M. Bronstein, M. M. Bronstein, and R. Kimmel, “Full and partial
symmetries of non-rigid shapes,” International Journal of Computer Vision,
vol. 89. Springer Nature, pp. 18–39, 2010.
ista: Raviv D, Bronstein AM, Bronstein MM, Kimmel R. 2010. Full and partial symmetries
of non-rigid shapes. International Journal of Computer Vision. 89, 18–39.
mla: Raviv, Dan, et al. “Full and Partial Symmetries of Non-Rigid Shapes.” International
Journal of Computer Vision, vol. 89, Springer Nature, 2010, pp. 18–39, doi:10.1007/s11263-010-0320-3.
short: D. Raviv, A.M. Bronstein, M.M. Bronstein, R. Kimmel, International Journal
of Computer Vision 89 (2010) 18–39.
date_created: 2024-10-15T11:20:54Z
date_published: 2010-08-01T00:00:00Z
date_updated: 2024-11-12T08:30:11Z
day: '01'
doi: 10.1007/s11263-010-0320-3
extern: '1'
intvolume: ' 89'
language:
- iso: eng
month: '08'
oa_version: None
page: 18-39
publication: International Journal of Computer Vision
publication_identifier:
eissn:
- 1573-1405
issn:
- 0920-5691
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Full and partial symmetries of non-rigid shapes
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 89
year: '2010'
...
---
OA_place: publisher
OA_type: free access
_id: '18360'
abstract:
- lang: eng
text: Many manifold learning procedures try to embed a given feature data into a
flat space of low dimensionality while preserving as much as possible the metric
in the natural feature space. The embedding process usually relies on distances
between neighboring features, mainly since distances between features that are
far apart from each other often provide an unreliable estimation of the true distance
on the feature manifold due to its non-convexity. Distortions resulting from using
long geodesics indiscriminately lead to a known limitation of the Isomap algorithm
when used to map non-convex manifolds. Presented is a framework for nonlinear
dimensionality reduction that uses both local and global distances in order to
learn the intrinsic geometry of flat manifolds with boundaries. The resulting
algorithm filters out potentially problematic distances between distant feature
points based on the properties of the geodesics connecting those points and their
relative distance to the boundary of the feature manifold, thus avoiding an inherent
limitation of the Isomap algorithm. Since the proposed algorithm matches non-local
structures, it is robust to strong noise. We show experimental results demonstrating
the advantages of the proposed approach over conventional dimensionality reduction
techniques, both global and local in nature.
article_processing_charge: No
article_type: original
author:
- first_name: Guy
full_name: Rosman, Guy
last_name: Rosman
- first_name: Michael M.
full_name: Bronstein, Michael M.
last_name: Bronstein
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Ron
full_name: Kimmel, Ron
last_name: Kimmel
citation:
ama: Rosman G, Bronstein MM, Bronstein AM, Kimmel R. Nonlinear dimensionality reduction
by topologically constrained isometric embedding. International Journal of
Computer Vision. 2010;89:56-68. doi:10.1007/s11263-010-0322-1
apa: Rosman, G., Bronstein, M. M., Bronstein, A. M., & Kimmel, R. (2010). Nonlinear
dimensionality reduction by topologically constrained isometric embedding. International
Journal of Computer Vision. Springer Nature. https://doi.org/10.1007/s11263-010-0322-1
chicago: Rosman, Guy, Michael M. Bronstein, Alex M. Bronstein, and Ron Kimmel. “Nonlinear
Dimensionality Reduction by Topologically Constrained Isometric Embedding.” International
Journal of Computer Vision. Springer Nature, 2010. https://doi.org/10.1007/s11263-010-0322-1.
ieee: G. Rosman, M. M. Bronstein, A. M. Bronstein, and R. Kimmel, “Nonlinear dimensionality
reduction by topologically constrained isometric embedding,” International
Journal of Computer Vision, vol. 89. Springer Nature, pp. 56–68, 2010.
ista: Rosman G, Bronstein MM, Bronstein AM, Kimmel R. 2010. Nonlinear dimensionality
reduction by topologically constrained isometric embedding. International Journal
of Computer Vision. 89, 56–68.
mla: Rosman, Guy, et al. “Nonlinear Dimensionality Reduction by Topologically Constrained
Isometric Embedding.” International Journal of Computer Vision, vol. 89,
Springer Nature, 2010, pp. 56–68, doi:10.1007/s11263-010-0322-1.
short: G. Rosman, M.M. Bronstein, A.M. Bronstein, R. Kimmel, International Journal
of Computer Vision 89 (2010) 56–68.
date_created: 2024-10-15T11:20:54Z
date_published: 2010-08-01T00:00:00Z
date_updated: 2024-11-12T08:25:56Z
day: '01'
doi: 10.1007/s11263-010-0322-1
extern: '1'
intvolume: ' 89'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1007/s11263-010-0322-1
month: '08'
oa: 1
oa_version: Published Version
page: 56-68
publication: International Journal of Computer Vision
publication_identifier:
eissn:
- 1573-1405
issn:
- 0920-5691
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Nonlinear dimensionality reduction by topologically constrained isometric embedding
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 89
year: '2010'
...
---
OA_type: closed access
_id: '18356'
abstract:
- lang: eng
text: Similarity is one of the most important abstract concepts in human perception
of the world. In computer vision, numerous applications deal with comparing objects
observed in a scene with some a priori known patterns. Often, it happens that
while two objects are not similar, they have large similar parts, that is, they
are partially similar. Here, we present a novel approach to quantify partial similarity
using the notion of Pareto optimality. We exemplify our approach on the problems
of recognizing non-rigid geometric objects, images, and analyzing text sequences.
article_processing_charge: No
article_type: original
author:
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Michael M.
full_name: Bronstein, Michael M.
last_name: Bronstein
- first_name: Alfred M.
full_name: Bruckstein, Alfred M.
last_name: Bruckstein
- first_name: Ron
full_name: Kimmel, Ron
last_name: Kimmel
citation:
ama: Bronstein AM, Bronstein MM, Bruckstein AM, Kimmel R. Partial similarity of
objects, or how to compare a centaur to a horse. International Journal of Computer
Vision. 2009;84(2):163-183. doi:10.1007/s11263-008-0147-3
apa: Bronstein, A. M., Bronstein, M. M., Bruckstein, A. M., & Kimmel, R. (2009).
Partial similarity of objects, or how to compare a centaur to a horse. International
Journal of Computer Vision. Springer Nature. https://doi.org/10.1007/s11263-008-0147-3
chicago: Bronstein, Alex M., Michael M. Bronstein, Alfred M. Bruckstein, and Ron
Kimmel. “Partial Similarity of Objects, or How to Compare a Centaur to a Horse.”
International Journal of Computer Vision. Springer Nature, 2009. https://doi.org/10.1007/s11263-008-0147-3.
ieee: A. M. Bronstein, M. M. Bronstein, A. M. Bruckstein, and R. Kimmel, “Partial
similarity of objects, or how to compare a centaur to a horse,” International
Journal of Computer Vision, vol. 84, no. 2. Springer Nature, pp. 163–183,
2009.
ista: Bronstein AM, Bronstein MM, Bruckstein AM, Kimmel R. 2009. Partial similarity
of objects, or how to compare a centaur to a horse. International Journal of Computer
Vision. 84(2), 163–183.
mla: Bronstein, Alex M., et al. “Partial Similarity of Objects, or How to Compare
a Centaur to a Horse.” International Journal of Computer Vision, vol. 84,
no. 2, Springer Nature, 2009, pp. 163–83, doi:10.1007/s11263-008-0147-3.
short: A.M. Bronstein, M.M. Bronstein, A.M. Bruckstein, R. Kimmel, International
Journal of Computer Vision 84 (2009) 163–183.
date_created: 2024-10-15T11:20:54Z
date_published: 2009-08-01T00:00:00Z
date_updated: 2024-10-22T07:55:59Z
day: '01'
doi: 10.1007/s11263-008-0147-3
extern: '1'
intvolume: ' 84'
issue: '2'
language:
- iso: eng
month: '08'
oa_version: None
page: 163-183
publication: International Journal of Computer Vision
publication_identifier:
eissn:
- 1573-1405
issn:
- 0920-5691
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Partial similarity of objects, or how to compare a centaur to a horse
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 84
year: '2009'
...
---
OA_type: closed access
_id: '18357'
abstract:
- lang: eng
text: 'This paper explores the problem of similarity criteria between nonrigid shapes.
Broadly speaking, such criteria are divided into intrinsic and extrinsic, the
first referring to the metric structure of the object and the latter to how it
is laid out in the Euclidean space. Both criteria have their advantages and disadvantages:
extrinsic similarity is sensitive to nonrigid deformations, while intrinsic similarity
is sensitive to topological noise. In this paper, we approach the problem from
the perspective of metric geometry. We show that by unifying the extrinsic and
intrinsic similarity criteria, it is possible to obtain a stronger topology-invariant
similarity, suitable for comparing deformed shapes with different topology. We
construct this new joint criterion as a tradeoff between the extrinsic and intrinsic
similarity and use it as a set-valued distance. Numerical results demonstrate
the efficiency of our approach in cases where using either extrinsic or intrinsic
criteria alone would fail.'
article_processing_charge: No
article_type: original
author:
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Michael M.
full_name: Bronstein, Michael M.
last_name: Bronstein
- first_name: Ron
full_name: Kimmel, Ron
last_name: Kimmel
citation:
ama: Bronstein AM, Bronstein MM, Kimmel R. Topology-invariant similarity of nonrigid
shapes. International Journal of Computer Vision. 2009;81(3):281-301. doi:10.1007/s11263-008-0172-2
apa: Bronstein, A. M., Bronstein, M. M., & Kimmel, R. (2009). Topology-invariant
similarity of nonrigid shapes. International Journal of Computer Vision.
Springer Nature. https://doi.org/10.1007/s11263-008-0172-2
chicago: Bronstein, Alex M., Michael M. Bronstein, and Ron Kimmel. “Topology-Invariant
Similarity of Nonrigid Shapes.” International Journal of Computer Vision.
Springer Nature, 2009. https://doi.org/10.1007/s11263-008-0172-2.
ieee: A. M. Bronstein, M. M. Bronstein, and R. Kimmel, “Topology-invariant similarity
of nonrigid shapes,” International Journal of Computer Vision, vol. 81,
no. 3. Springer Nature, pp. 281–301, 2009.
ista: Bronstein AM, Bronstein MM, Kimmel R. 2009. Topology-invariant similarity
of nonrigid shapes. International Journal of Computer Vision. 81(3), 281–301.
mla: Bronstein, Alex M., et al. “Topology-Invariant Similarity of Nonrigid Shapes.”
International Journal of Computer Vision, vol. 81, no. 3, Springer Nature,
2009, pp. 281–301, doi:10.1007/s11263-008-0172-2.
short: A.M. Bronstein, M.M. Bronstein, R. Kimmel, International Journal of Computer
Vision 81 (2009) 281–301.
date_created: 2024-10-15T11:20:54Z
date_published: 2009-03-01T00:00:00Z
date_updated: 2024-10-22T07:27:15Z
day: '01'
doi: 10.1007/s11263-008-0172-2
extern: '1'
intvolume: ' 81'
issue: '3'
language:
- iso: eng
month: '03'
oa_version: None
page: 281-301
publication: International Journal of Computer Vision
publication_identifier:
eissn:
- 1573-1405
issn:
- 0920-5691
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Topology-invariant similarity of nonrigid shapes
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 81
year: '2009'
...
---
_id: '18354'
abstract:
- lang: eng
text: "An expression-invariant 3D face recognition approach is presented. Our basic
assumption is that facial expressions can be modelled as isometries of the facial
surface. This allows to construct expression-invariant representations of faces
using the bending-invariant canonical forms approach. The result is an efficient
and accurate face recognition algorithm, robust to facial expressions, that can
distinguish between identical twins (the first two authors). We demonstrate a
prototype system based on the proposed algorithm and compare its performance to
classical face recognition methods.\r\n\r\nThe numerical methods employed by our
approach do not require the facial surface explicitly. The surface gradients field,
or the surface metric, are sufficient for constructing the expression-invariant
representation of any given face. It allows us to perform the 3D face recognition
task while avoiding the surface reconstruction stage."
article_processing_charge: No
article_type: original
author:
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Michael M.
full_name: Bronstein, Michael M.
last_name: Bronstein
- first_name: Ron
full_name: Kimmel, Ron
last_name: Kimmel
citation:
ama: Bronstein AM, Bronstein MM, Kimmel R. Three-dimensional face recognition. International
Journal of Computer Vision. 2005;64(1):5-30. doi:10.1007/s11263-005-1085-y
apa: Bronstein, A. M., Bronstein, M. M., & Kimmel, R. (2005). Three-dimensional
face recognition. International Journal of Computer Vision. Springer Nature.
https://doi.org/10.1007/s11263-005-1085-y
chicago: Bronstein, Alex M., Michael M. Bronstein, and Ron Kimmel. “Three-Dimensional
Face Recognition.” International Journal of Computer Vision. Springer Nature,
2005. https://doi.org/10.1007/s11263-005-1085-y.
ieee: A. M. Bronstein, M. M. Bronstein, and R. Kimmel, “Three-dimensional face recognition,”
International Journal of Computer Vision, vol. 64, no. 1. Springer Nature,
pp. 5–30, 2005.
ista: Bronstein AM, Bronstein MM, Kimmel R. 2005. Three-dimensional face recognition.
International Journal of Computer Vision. 64(1), 5–30.
mla: Bronstein, Alex M., et al. “Three-Dimensional Face Recognition.” International
Journal of Computer Vision, vol. 64, no. 1, Springer Nature, 2005, pp. 5–30,
doi:10.1007/s11263-005-1085-y.
short: A.M. Bronstein, M.M. Bronstein, R. Kimmel, International Journal of Computer
Vision 64 (2005) 5–30.
date_created: 2024-10-15T11:20:54Z
date_published: 2005-04-01T00:00:00Z
date_updated: 2024-10-21T09:08:17Z
day: '01'
doi: 10.1007/s11263-005-1085-y
extern: '1'
intvolume: ' 64'
issue: '1'
language:
- iso: eng
month: '04'
oa_version: None
page: 5-30
publication: International Journal of Computer Vision
publication_identifier:
eissn:
- 1573-1405
issn:
- 0920-5691
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Three-dimensional face recognition
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 64
year: '2005'
...