A high speed procam and its application to particle display

2020 Fiscal Year Final Research Report

• Project/Area Number: 16H02856
• Research Category: Grant-in-Aid for Scientific Research (B)
• Allocation Type: Single-year Grants
• Section: 一般
• Research Field: Human interface and interaction
• Research Institution: Tokyo Institute of Technology
• Principal Investigator: Koike Hideki 東京工業大学, 情報理工学院, 教授 (70234664)
• Project Period (FY): 2016-04-01 – 2020-03-31
• Keywords: 立体ディスプレイ / モーションキャプチャ / 実時間追跡 / プロジェクション

Outline of Final Research Achievements

Unlike projection mapping on a static object, projection on a fast moving object causes a delay in projection. Previous research has proposed a method to predict the position of an object using a Kalman filter, but this method does not deal with projections onto objects with three-dimensional shapes. In this study, we attempted to solve the above delay by learning the object's motion using deep learning, simultaneously predicting the object's position and 3D posture in about 0.5 seconds, and projecting the object. As a result of the experiment, we were able to improve the projection accuracy compared to the case of no prediction and Kalman filter prediction only.

Free Research Field

ヒューマンコンピュータインタラクション

Academic Significance and Societal Importance of the Research Achievements

近年，プロジェクションマッピングが一般化しているが，これらは建築物や低速で移動する物体のみを対象としていた．これに対し，本研究は，高速に移動する任意の形状の物体への正確なプロジェクションマッピングを実現した．本研究成果は，今後，立体ディスプレイへの応用，さらには，舞台芸術，テーマパーク，さらにはスポーツ等への応用が期待できる．