Welcome

Our lab studies on computer graphics and computer vision techniques, focusing on image, video, and geometry processing for 2D and 3D real-world data and their purpose-specific visualization. You can find recent work of our lab in the following list.

You can also find the entire list of projects from the [RESEARCH] page.

The School of Social Data Science, Hitotsubashi University is actively recruiting both the undergraduate and post-graduate students. Visit our official webpage at (https://www.sds.hit-u.ac.jp/).

If you wish to join our faculty, please feel free to contact me with the contact form. If you wish to enter the graduate school and to join my laboratory, it would be a good idea to contact me in advance. Then, let's have a brief meeting to talk about your research interest.

News

Recent Research

NeMCoF: Neural Material Composition Fields for Material Decomposition in Sparse-View Spectral X-ray CT

Reference: Takumi Hotta, Tatsuya Yatagawa, Yutaka Ohtake, Toru Aoki, “NeMCoF: Neural Material Composition Fields for Material Decomposition in Sparse-View Spectral X-ray CT,” Journal of Nondestructive Evaluation (2025).

Towards accelerating polarization path tracing of multi-bounce Smith microfacet BSDFs

Reference: Hidehito Ohba, Tatsuya Yatagawa, Shigeo Morishima, “Towards accelerating polarization path tracing of multi-bounce Smith microfacet BSDFs,” SIGGRAPH 2025 Posters (2025).

Learning Scatter Artifact Correction in Cone-Beam X-Ray CT Using Incomplete Projections with Beam Hole Array

Reference: Haruki Hattori, Tatsuya Yatagawa, Yutaka Ohtake, Hiromasa Suzuki, “Learning Scatter Artifact Correction in Cone-Beam X-Ray CT Using Incomplete Projections with Beam Hole Array,” Journal of Nondestructive Evaluation (2024).

X-ray 3D Fiber Orientation Tomography via Alternating Optimization of Scattering Coefficients and Directions

Reference: Tomoki Mori, Yutaka Ohtake, Tatsuya Yatagawa, Kazuhiro Kido, Yasunori Tsuboi, “X-ray 3D Fiber Orientation Tomography via Alternating Optimization of Scattering Coefficients and Directions,” Journal of Nondestructive Evaluation (2024).

Learning Self-Prior for Mesh Inpainting Using Self-Supervised Graph Convolutional Networks

Reference: Shota Hattori, Tatsuya Yatagawa, Yutaka Ohtake, Hiromasa Suzuki, “Learning Self-Prior for Mesh Inpainting Using Self-Supervised Graph Convolutional Networks,” IEEE Transactions on Visualization and Computer Graphics (2024).

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