Shingo Ando — accepted AI-conference papers

2023

Unsupervised Intrinsic Image Decomposition With LiDAR Intensity

Shogo Sato, Yasuhiro Yao, Taiga Yoshida, Takuhiro Kaneko, Shingo Ando, Jun Shimamura

CVPR 2023poster

Intrinsic image decomposition (IID) is the task that decomposes a natural image into albedo and shade. While IID is typically solved through supervised learning methods, it is not ideal due to the difficulty in observing ground truth albedo and shade in general scenes. Conversely, unsupervised learn…

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2020

Discontinuous and Smooth Depth Completion With Binary Anisotropic Diffusion Tensor

Yasuhiro Yao, Menandro Roxas, Ryoichi Ishikawa, Shingo Ando, Jun Shimamura, Takeshi Oishi

RA-L 2020

We propose an unsupervised real-time dense depth completion from a sparse depth map guided by a single image. Our method generates a smooth depth map while preserving discontinuity between different objects. Our key idea is a Binary Anisotropic Diffusion Tensor (B-ADT) which can completely eliminate

Cited by 12 Source ↗Scholar ↗