1 code implementation • CVPR 2023 • Xu Cao, Hiroaki Santo, Fumio Okura, Yasuyuki Matsushita
We present a method for 3D reconstruction only using calibrated multi-view surface azimuth maps.
no code implementations • 14 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.
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.
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.
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.
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.
no code implementations • 27 Nov 2020 • Takuma Doi, Fumio Okura, Toshiki Nagahara, Yasuyuki Matsushita, Yasushi Yagi
This paper proposes a multi-view extension of instance segmentation without relying on texture or shape descriptor matching.
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.