概要
We propose an efficient pipeline from input to output for a tensor light-field display. Conventionally, a dense light field (i.e., tens of images taken with narrow viewpoint intervals) is required as an input in such displays. However, obtaining dense light fields is a challenging task for real scenes. To make the acquisition process more efficient, we adopted a coded-aperture camera as an input device, which is suitable for acquiring dense light fields in a compressive manner. Moreover, we modeled the entire process from acquisition to display using a convolutional neural network. As a result of training the network on a massive light field data, we can reproduce the whole light field on the display from only a few images taken with the camera. Both simulative and real experiments were conducted to show the effectiveness of our method.
長原一
教授
コンピューテーショナルフォトグラフィ、コンピュータビジョンを専門とし実世界センシングや情報処理技術、画像認識技術の研究を行う。さらに、画像センシングにとどまらず様々なセンサに拡張したコンピュテーショナルセンシング手法の開発や高次元で冗長な実世界ビッグデータから意味のある情報を計測するスパースセンシングへの転換を目指す。