Kota Suzui

About

Kota Suzui is a researcher whose work lies at the intersection of computer vision and deep learning, with a particular focus on 3D object understanding. His most notable contribution is in the domain of 6 Degrees of Freedom (6 DOF) object pose estimation—a critical task for robotics and augmented reality. In his 2019 paper, Suzui proposed a novel method that dramatically reduces the dataset size required for training, addressing a major bottleneck in the field. His approach introduces RotationCNN, a specialized convolutional neural network designed to predict an object’s 3D orientation, while leveraging a separate object detection CNN to estimate its 3D position. This dual-network strategy enables accurate pose estimation from minimal data, making it highly practical for real-world applications where large annotated datasets are unavailable. Though early in its citation impact, this work has laid a foundation for more efficient, data-light pose estimation systems. Suzui’s research demonstrates a clear commitment to solving practical challenges in computer vision, offering a path toward scalable and accessible 3D perception technologies.

Research Focus

Key Achievements

Top Papers

1. 1
   Toward 6 DOF Object Pose Estimation with Minimum Dataset

   4 citations · 2019

Key Collaborators

Contact & Links