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Pierre-André Brousseau

dblp:259/5330 · DBLP profile ↗
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3ranked-venue papers
3as first author
2since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 3 · 3 first-author · 2 since 2021Graphics, computer vision, multimedia, augmented reality and games · 3 · 3 first-author · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
2 papers
3D vision · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision
depth estimation
0.912025
Spherical Epipolar Rectification for Deep Two-View Absolute Depth Estimation · ICCV 2025
Computer vision › 3D vision › multi-view geometry
epipolar geometry
0.912025
Spherical Epipolar Rectification for Deep Two-View Absolute Depth Estimation · ICCV 2025
Computer vision › 3D vision
camera calibration
0.412019
Calibration of Axial Fisheye Cameras Through Generic Virtual Central Models · ICCV 2019
Computer vision › 3D vision › camera calibration
fisheye camera calibration
0.412019
Calibration of Axial Fisheye Cameras Through Generic Virtual Central Models · ICCV 2019

Methods — techniques the papers use, named apart from their topics

deep two-view estimation · 0.9structured light · 0.4generic virtual central model · 0.4
YearPublicationVenuePosition
2025 Spherical Epipolar Rectification for Deep Two-View Absolute Depth Estimation
Pierre-André Brousseau, Sébastien Roy 0001
ICCV1
2023 Strong Stereo Features for Self-Supervised Practical Stereo Matching
Pierre-André Brousseau, Sébastien Roy 0001
BMVC1
2019 Calibration of Axial Fisheye Cameras Through Generic Virtual Central Models
abstract
Fisheye cameras are notoriously hard to calibrate using traditional plane-based methods. This paper proposes a new calibration method for large field of view cameras. Similarly to planar calibration, it relies on multiple images of a planar calibration grid with dense correspondences, typically obtained using structured light. By relying on the grids themselves instead of the distorted image plane, we can build a rectilinear Generic Virtual Central (GVC) camera. Instead of relying on a single GVC camera, our method proposes a selection of multiple GVC cameras which can cover any field of view and be trivially aligned to provide a very accurate generic central model. We demonstrate that this approach can directly model axial cameras, assuming the distortion center is located on the camera axis. Experimental validation is provided on both synthetic and real fisheye cameras featuring up to a 280° field of view. To our knowledge, this is one of the only practical methods to calibrate axial cameras.
Pierre-André Brousseau, Sébastien Roy 0001
ICCV1