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Zhong-Dan Lan

dblp:05/3696 · DBLP profile ↗
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6ranked-venue papers
4as first author
0since 2021 · last 1999
—ORCID · none

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

Artificial intelligence and machine learning · 5 · 3 first-authorGraphics, computer vision, multimedia, augmented reality and games · 4 · 3 first-author

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%
Theoretical computer science
1 paper
Computational geometry · 100%
Computer graphics and multimedia
2 papers
Geometric modeling and processing · 100%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision
camera pose estimation
0.021999
Linear N-Point Camera Pose Determination · IEEE Trans. Pattern Anal. Mach. Intell. 1999
Linear N>=4-Point Pose Determination · ICCV 1998
Computer vision › 3D vision › multi-view geometry
camera geometry
0.011999
Linear N-Point Camera Pose Determination · IEEE Trans. Pattern Anal. Mach. Intell. 1999
Computer vision › 3D vision
multi-view geometry
0.011999
Linear N-Point Camera Pose Determination · IEEE Trans. Pattern Anal. Mach. Intell. 1999
Computational geometry › geometric vision › multi-view geometry
pose estimation
0.011998
Linear N>=4-Point Pose Determination · ICCV 1998
Geometric modeling and processing
3d reconstruction
0.011999
Linear N-Point Camera Pose Determination · IEEE Trans. Pattern Anal. Mach. Intell. 1999
Geometric modeling and processing
projective geometry
0.011998
Linear N>=4-Point Pose Determination · ICCV 1998

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

space resection · 0.1linear solution · 0.1linear algorithm · 0.0
YearPublicationVenuePosition
1999 Linear N-Point Camera Pose Determination
abstract
The determination of camera position and orientation from known correspondences of 3D reference points and their images is known as pose estimation in computer vision and space resection in photogrammetry. It is well-known that from three corresponding points there are at most four algebraic solutions. Less appears to be known about the cases of four and five corresponding points. We propose a family of linear methods that yield a unique solution to 4- and 5-point pose determination for generic reference points. We first review the 3-point algebraic method. Then we present our two-step, 4-point and one-step, 5-point linear algorithms. The 5-point method can also be extended to handle more than five points. Finally, we demonstrate our methods on both simulated and real images. We show that they do not degenerate for coplanar configurations and even outperform the special linear algorithm for coplanar configurations in practice.
Long Quan, Zhong-Dan Lan
IEEE Trans. Pattern Anal. Mach. Intell.2
1998 Precise Matching by Robust Estimation of Deformation and Local Coherence
Zhong-Dan Lan, Roger Mohr, Long Quan
ACCV (1)1
1998 Linear N>=4-Point Pose Determination
abstract
The determination of the position and the orientation of the camera from the known correspondences of the reference points and the image points is known as the problem of pose estimation in computer vision or space resection in photogrammetry. It is well known that using 3 corresponding points has at most 4 solutions. Less appears to be known about the cases of 4 and 5 points. In this paper, we describe linear solutions that always give the unique solution to 4-point and 5-point pose determination for the reference points not lying on the critical configurations. The same linear method can also be extended to any n/spl ges/5 points. The robustness and accuracy of the method are experimented both on simulated and real images.
Long Quan, Zhong-Dan Lan
ICCV2
1998 Direct linear sub-pixel correlation by incorporation of neighbor pixels' information and robust estimation of window transformation
Zhong-Dan Lan, Roger Mohr
Mach. Vis. Appl.1
1997 Robust Location Based Partial Correlation
Zhong-Dan Lan, Roger Mohr
CAIP1
1995 Robust Matching by Partial Correlation
abstract
International audience
Zhong-Dan Lan, Roger Mohr, Paolo Remagnino
BMVC1