Depth Estimation fusing Image and Radar Measurements with Uncertain Directions

no code implementations • 23 Mar 2024 • Masaya Kotani, Takeru Oba, Norimichi Ukita

This paper proposes a depth estimation method using radar-image fusion by addressing the uncertain vertical directions of sparse radar measurements.

Depth Estimation

R2-Diff: Denoising by diffusion as a refinement of retrieved motion for image-based motion prediction

no code implementations • 15 Jun 2023 • Takeru Oba, Norimichi Ukita

In R2-Diff, a motion retrieved from a dataset based on image similarity is fed into a diffusion model instead of random noise.

Denoising motion prediction +2

Data-Driven Stochastic Motion Evaluation and Optimization with Image by Spatially-Aligned Temporal Encoding

no code implementations • 10 Feb 2023 • Takeru Oba, Norimichi Ukita

While our method evaluates the task achievability by the Energy-Based Model (EBM), previous EBMs are not designed for evaluating the consistency between different domains (i. e., image and motion in our method).

motion prediction

NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results

no code implementations • 7 Jun 2021 • Goutam Bhat, Martin Danelljan, Radu Timofte, Kazutoshi Akita, Wooyeong Cho, Haoqiang Fan, Lanpeng Jia, Daeshik Kim, Bruno Lecouat, Youwei Li, Shuaicheng Liu, Ziluan Liu, Ziwei Luo, Takahiro Maeda, Julien Mairal, Christian Micheloni, Xuan Mo, Takeru Oba, Pavel Ostyakov, Jean Ponce, Sanghyeok Son, Jian Sun, Norimichi Ukita, Rao Muhammad Umer, Youliang Yan, Lei Yu, Magauiya Zhussip, Xueyi Zou

This paper reviews the NTIRE2021 challenge on burst super-resolution.

Super-Resolution