Projects
これまでに発表した具体的な研究成果を、プロジェクトごとに紹介しています。
ボタンを押すと、プロジェクトを研究分野で絞り込めます。
NDT
applications
出典:
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).
graphics
rendering
出典:
Hidehito Ohba, Tatsuya Yatagawa, Shigeo Morishima, “Towards accelerating polarization path tracing of multi-bounce Smith microfacet BSDFs,” (2025).
NDT
applications
出典:
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).
NDT
applications
出典:
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).
graphics
geometry
deep learning
出典:
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).
graphics
rendering
出典:
Yuichiro Manabe, Tatsuya Yatagawa, Shigeo Morishima, Hiroyuki Kubo, “Monte Carlo Path Tracing and Statistical Event Detection for Event Camera Simulation,” (2024).
graphics
geometry
applications
出典:
Haruki Hattori, Yutaka Ohtake, Tatsuya Yatagawa, Kazutoshi Miyamoto, Masashi Hirota, “Primitive-based Model Reduction for Huge 3D Meshes of Industrial Plants,” (2024).
vision
deep learning
出典:
Sreenithy Chandran, Tatsuya Yatagawa, Hiroyuki Kubo, Suren Jayasuriya, “Learning-Based Spotlight Position Optimization for Non-Line-of-Sight Human Localization and Posture Classification,” (2024).
applications
NDT
出典:
Siqi Wang, Tatsuya Yatagawa, Yutaka Ohtake, Hiromasa Suzuki, “Sparse-View Cone-Beam CT Reconstruction by Bar-by-Bar Neural FDK Algorithm,” Nondestructive Testing and Evaluation (2023).
graphics
geometry
applications
出典:
Yuta Yamauchi, Tatsuya Yatagawa, Yutaka Ohtake, Hiromasa Suzuki, “Bin-scanning: Segmentation of X-ray CT volume of binned parts using Morse skeleton graph of distance transform,” Computational Visual Media (2023).
graphics
rendering
出典:
Yuta Tsuji, Tatsuya Yatagawa, Hiroyuki Kubo, Shigeo Morishima, “Event-Based Camera Simulation Using Monte Carlo Path Tracing with Adaptive Denoising,” (2023).
applications
NDT
出典:
Wang Siqi, Tatsuya Yatagawa, Yutaka Ohtake, Toru Aoki, Jun Hotta, “End-to-End Deep Learning for Reconstructing Segmented 3D CT Image ffrom Multi-Energy X-ray Projections,” (2023).
applications
NDT
出典:
Nobumichi Yasunami, Tatsuya Yatagawa, Hiromasa Suzuki, Yutaka Ohtake, “Degree of local symmetry for geometry-aware selective part visualisation on CT volumes,” Nondestructive Testing and Evaluation (2022).
applications
NDT
出典:
Siqi Wang, Tatsuya Yatagawa, Hiromasa Suzuki, Yutaka Ohtake, “Image Based Measurement of Individual Fiber Lengths for Randomly Oriented Short Fiber Composites,” Journal of Nondestructive Evaluation (2022).
graphics
rendering
出典:
Takahiro Kuge, Tatsuya Yatagawa, Shigeo Morishima, “Real-time Shading with Free-form Planar Area Lights using Linearly Transformed Cosines,” Journal of Computer Graphics Techniques (2022).
graphics
geometry
deep learning
出典:
Shota Hattori, Tatsuya Yatagawa, Yutaka Ohtake, Hiromasa Suzuki, “Learning Self-prior for Mesh Denoising using Dual Graph Convolutional Networks,” (2022).
graphics
rendering
deep learning
出典:
Yuta Tsuji, Tatsuya Yatagawa, Shigeo Morishima, “Light Source Selection in Primary-Sample-Space Photon Sampling,” (2021).
graphics
geometry
deep learning
出典:
Kiichi Itoh, Tatsuya Yatagawa, Yutaka Ohtake, Hiromasa Suzuki, “Extended Differentiable Marching Cubes by Manifold-Preserving Shape Inflation,” (2021).
graphics
geometry
deep learning
出典:
Shota Hattori, Tatsuya Yatagawa, Yutaka Ohtake, Hiromasa Suzuki, “Deep Mesh Prior: Unsupervised Mesh Restoration using Graph Convolutional Networks,” (2021).
graphics
rendering
出典:
Tatsuya Yatagawa, Yasushi Yamaguchi, Shigeo Morishima, “LinSSS: Linear decomposition of heterogeneous subsurface scattering for real-time screen-space rendering,” The Visual Computer (Special Issue of Computer Graphics International 2020) (2020).
graphics
rendering
出典:
Tatsuya Yatagawa, Hideki Todo, Yasushi Yamaguchi, Shigeo Morishima, “Data Compression for Measured Heterogeneous Subsurface Scattering via Scattering Profile Blending,” The Visual Computer (2020).
graphics
rendering
出典:
Tomoya Yamaguchi, Tatsuya Yatagawa, Yusuke Tokuyoshi, Shigeo Morishima, “Real-time rendering of layered materials with anisotorpic normal distributions,” Computational Visual Media (2020).
graphics
rendering
出典:
Takahiro Kuge, Tatsuya Yatagawa, Shigeo Morishima, “Real-time Indirect Illumination of Emissive Inhomogeneous Volumes using Layered Polygonal Area Lights,” Computer Graphics Forum (Special Issue of Pacific Graphics 2019) (2019).
image & video
deep learning
出典:
Ryota Natsume, Tatsuya Yatagawa, Shigeo Morishima, “FSNet: An Identity-aware Generative Model for Image-based Face Swapping,” (2018).
graphics
rendering
出典:
Tomoya Yamaguchi, Tatsuya Yatagawa, Shigeo Morishima, “Efficient Metropolis Path Sampling for Material Editing and Re-rendering,” (2018).
image & video
deep learning
出典:
Ryota Natsume, Tatsuya Yatagawa, Shigeo Morishima, “RSGAN: face swapping and editing via region separation in latent spaces,” (2018).
image & video
出典:
Tatsuya Yatagawa, Yasushi Yamaguchi, “Sparse Pixel Sampling for Appearance Edit Propagation,” The Visual Computer (Special Issue of Computer Graphics International 2015) (2015).
image & video
出典:
Tatsuya Yatagawa, Yasushi Yamaguchi, “Temporally Coherent Video Editing using an Edit Propagation Matrix,” Computers & Graphics (2014).
image & video
出典:
Tatsuya Yatagawa, Yasushi Yamaguchi, “A Template-Based Completion Framework for Videos with Dynamic Backgrounds,” (2012).