Search Results for author: Fumio Okura

Found 8 papers, 3 papers with code

Text-Guided Scene Sketch-to-Photo Synthesis

no code implementations14 Feb 2023 AprilPyone MaungMaung, Makoto Shing, Kentaro Mitsui, Kei Sawada, Fumio Okura

To this end, we leverage knowledge from recent large-scale pre-trained generative models, resulting in text-guided sketch-to-photo synthesis without the need for reference images.

Self-Supervised Learning

Multispectral Photometric Stereo for Spatially-Varying Spectral Reflectances: A Well Posed Problem?

1 code implementation CVPR 2021 Heng Guo, Fumio Okura, Boxin Shi, Takuya Funatomi, Yasuhiro Mukaigawa, Yasuyuki Matsushita

To make the problem well-posed, existing MPS methods rely on restrictive assumptions, such as shape prior, surfaces having a monochromatic with uniform albedo.

Normal Integration via Inverse Plane Fitting With Minimum Point-to-Plane Distance

1 code implementation CVPR 2021 Xu Cao, Boxin Shi, Fumio Okura, Yasuyuki Matsushita

Experimental results on analytically computed, synthetic, and real-world surfaces show that our method yields accurate and stable reconstruction for both orthographic and perspective normal maps.

Surface Reconstruction

A Closer Look at Rotation-Invariant Deep Point Cloud Analysis

no code implementations ICCV 2021 Feiran Li, Kent Fujiwara, Fumio Okura, Yasuyuki Matsushita

Recent progress in rotation-invariant point cloud analysis is mainly driven by converting point clouds into their respective canonical poses, and principal component analysis (PCA) is a practical tool to achieve this.

Generalized Shuffled Linear Regression

no code implementations ICCV 2021 Feiran Li, Kent Fujiwara, Fumio Okura, Yasuyuki Matsushita

Therefore, in this work, we generalize the formulation of shuffled linear regression to a broader range of conditions where only part of the data should correspond.

regression

Probabilistic Plant Modeling via Multi-View Image-to-Image Translation

no code implementations CVPR 2018 Takahiro Isokane, Fumio Okura, Ayaka Ide, Yasuyuki Matsushita, Yasushi Yagi

This paper describes a method for inferring three-dimensional (3D) plant branch structures that are hidden under leaves from multi-view observations.

Image-to-Image Translation Translation

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