151 citations · 283 across the 12 of their papers we have counts for
14 papers
CrystalFramer: Rethinking the Role of Frames for SE(3)-Invariant Crystal Structure Modeling
Yusei Ito, Tatsunori Taniai, Ryo Igarashi +2
Crystal structure modeling with graph neural networks is essential for various applications in materials informatics, and capturing SE(3)-invariant geometric features is a fundamen…
Bridging Text and Crystal Structures: Literature-driven Contrastive Learning for Materials Science
Yuta Suzuki, Tatsunori Taniai, Ryo Igarashi +4
Understanding structure-property relationships is an essential yet challenging aspect of materials discovery and development. To facilitate this process, recent studies in material…
Deep Probabilistic Traversability with Test-time Adaptation for Uncertainty-aware Planetary Rover Navigation
Masafumi Endo, Tatsunori Taniai, Genya Ishigami
Traversability assessment of deformable terrain is vital for safe rover navigation on planetary surfaces. Machine learning (ML) is a powerful tool for traversability prediction but…
Crystalformer: Infinitely Connected Attention for Periodic Structure Encoding
Tatsunori Taniai, Ryo Igarashi, Yuta Suzuki +4
Predicting physical properties of materials from their crystal structures is a fundamental problem in materials science. In peripheral areas such as the prediction of molecular pro…
A Transformer Model for Symbolic Regression towards Scientific Discovery
Florian Lalande, Yoshitomo Matsubara, Naoya Chiba +3
Symbolic Regression (SR) searches for mathematical expressions which best describe numerical datasets. This allows to circumvent interpretation issues inherent to artificial neural…
Risk-aware Path Planning via Probabilistic Fusion of Traversability Prediction for Planetary Rovers on Heterogeneous Terrains
Masafumi Endo, Tatsunori Taniai, Ryo Yonetani +1
Machine learning (ML) plays a crucial role in assessing traversability for autonomous rover operations on deformable terrains but suffers from inevitable prediction errors. Especia…
Neural Structure Fields with Application to Crystal Structure Autoencoders
Naoya Chiba, Yuta Suzuki, Tatsunori Taniai +4
Representing crystal structures of materials to facilitate determining them via neural networks is crucial for enabling machine-learning applications involving crystal structure es…
Quasistatic contact-rich manipulation via linear complementarity quadratic programming
Sotaro Katayama, Tatsunori Taniai, Kazutoshi Tanaka
Contact-rich manipulation is challenging due to dynamically-changing physical constraints by the contact mode changes undergone during manipulation. This paper proposes a versatile…
Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery
Yoshitomo Matsubara, Naoya Chiba, Ryo Igarashi +1
This paper revisits datasets and evaluation criteria for Symbolic Regression (SR), specifically focused on its potential for scientific discovery. Focused on a set of formulas used…
Path Planning using Neural A* Search
Ryo Yonetani, Tatsunori Taniai, Mohammadamin Barekatain +2
We present Neural A*, a novel data-driven search method for path planning problems. Despite the recent increasing attention to data-driven path planning, machine learning approache…
Neural Inverse Rendering for General Reflectance Photometric Stereo
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…
Semi-Global Stereo Matching with Surface Orientation Priors
Daniel Scharstein, Tatsunori Taniai, Sudipta N. Sinha
Semi-Global Matching (SGM) is a widely-used efficient stereo matching technique. It works well for textured scenes, but fails on untextured slanted surfaces due to its fronto-paral…
Fast Multi-frame Stereo Scene Flow with Motion Segmentation
Tatsunori Taniai, Sudipta N. Sinha, Yoichi Sato
We propose a new multi-frame method for efficiently computing scene flow (dense depth and optical flow) and camera ego-motion for a dynamic scene observed from a moving stereo came…
Continuous 3D Label Stereo Matching using Local Expansion Moves
Tatsunori Taniai, Yasuyuki Matsushita, Yoichi Sato +1
We present an accurate stereo matching method using local expansion moves based on graph cuts. This new move-making scheme is used to efficiently infer per-pixel 3D plane labels on…