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Seungyong Lee

POSTECH

4 papers hereh-index 407.8k citations117 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author1
  • last author2

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV2
  • cs.GR2
affiliations
  • POSTECH
  • Pohang University of Science and Technology
Homepage
same name
  • Seungyong Lee — 12 papers, h 20
  • Seungyong Lee — 3 papers, h 1
  • Seungyong Lee — 3 papers, h 3
  • Seungyong Lee — 3 papers, h 2
  • Seungyong Lee — 2 papers, h 4
  • Seungyong Lee — 2 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedLaplacianFusion: Detailed 3D Clothed-Human Body Reconstruction

14 citations · 22 across the 4 of their papers we have counts for

collaborators

4 papers

cs.GR2023★ 8 cited

Mesh Density Adaptation for Template-based Shape Reconstruction

Yucheol Jung, Hyomin Kim, Gyeongha Hwang +2

In 3D shape reconstruction based on template mesh deformation, a regularization, such as smoothness energy, is employed to guide the reconstruction into a desirable direction. In t…

cs.CV2023

Differentiable Display Photometric Stereo

Seokjun Choi, Seungwoo Yoon, Giljoo Nam +2

Photometric stereo leverages variations in illumination conditions to reconstruct surface normals. Display photometric stereo, which employs a conventional monitor as an illuminati…

cs.GR2023★ 14 cited

LaplacianFusion: Detailed 3D Clothed-Human Body Reconstruction

Hyomin Kim, Hyeonseo Nam, Jungeon Kim +2

We propose LaplacianFusion, a novel approach that reconstructs detailed and controllable 3D clothed-human body shapes from an input depth or 3D point cloud sequence. The key idea o…

cs.CV2016

Convergence Analysis of MAP based Blur Kernel Estimation

Sunghyun Cho, Seungyong Lee

One popular approach for blind deconvolution is to formulate a maximum a posteriori (MAP) problem with sparsity priors on the gradients of the latent image, and then alternatingly…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.