no code implementations • 18 Mar 2024 • Tatsunori Taniai, Ryo Igarashi, Yuta Suzuki, Naoya Chiba, Kotaro Saito, Yoshitaka Ushiku, Kanta Ono
Predicting physical properties of materials from their crystal structures is a fundamental problem in materials science.
1 code implementation • 7 Dec 2023 • Florian Lalande, Yoshitomo Matsubara, Naoya Chiba, Tatsunori Taniai, Ryo Igarashi, Yoshitaka Ushiku
Once trained, we apply our best model to the SRSD datasets (Symbolic Regression for Scientific Discovery datasets) which yields state-of-the-art results using the normalized tree-based edit distance, at no extra computational cost.
no code implementations • 2 Mar 2023 • Masafumi Endo, Tatsunori Taniai, Ryo Yonetani, Genya Ishigami
Machine learning (ML) plays a crucial role in assessing traversability for autonomous rover operations on deformable terrains but suffers from inevitable prediction errors.
1 code implementation • 8 Dec 2022 • Naoya Chiba, Yuta Suzuki, Tatsunori Taniai, Ryo Igarashi, Yoshitaka Ushiku, Kotaro Saito, Kanta Ono
We propose neural structure fields (NeSF) as an accurate and practical approach for representing crystal structures using neural networks.
4 code implementations • 16 Sep 2020 • Ryo Yonetani, Tatsunori Taniai, Mohammadamin Barekatain, Mai Nishimura, Asako Kanezaki
We present Neural A*, a novel data-driven search method for path planning problems.
no code implementations • ICML 2018 • Tatsunori Taniai, Takanori Maehara
We present a novel convolutional neural network architecture for photometric stereo (Woodham, 1980), a problem of recovering 3D object surface normals from multiple images observed under varying illuminations.
no code implementations • 3 Dec 2017 • Daniel Scharstein, Tatsunori Taniai, Sudipta N. Sinha
In this paper we evaluate plane orientation priors derived from stereo matching at a coarser resolution and show that such priors can yield significant performance gains for difficult weakly-textured scenes.
no code implementations • CVPR 2017 • Tatsunori Taniai, Sudipta N. Sinha, Yoichi Sato
This unified framework benefits all four tasks - stereo, optical flow, visual odometry and motion segmentation leading to overall higher accuracy and efficiency.
no code implementations • CVPR 2016 • Tatsunori Taniai, Sudipta N. Sinha, Yoichi Sato
We propose a new technique to jointly recover cosegmentation and dense per-pixel correspondence in two images.
2 code implementations • 28 Mar 2016 • Tatsunori Taniai, Yasuyuki Matsushita, Yoichi Sato, Takeshi Naemura
The local expansion moves extend traditional expansion moves by two ways: localization and spatial propagation.
no code implementations • CVPR 2015 • Tatsunori Taniai, Yasuyuki Matsushita, Takeshi Naemura
We then present our method as generalization of SSP, which is further shown to generalize several state-of-the-art techniques for higher-order and pairwise non-submodular functions [Ayed13, Gorelick14, Tang14].
no code implementations • CVPR 2014 • Tatsunori Taniai, Yasuyuki Matsushita, Takeshi Naemura
We present an accurate and efficient stereo matching method using locally shared labels, a new labeling scheme that enables spatial propagation in MRF inference using graph cuts.