Semi-supervised angular super-resolution method for autostereoscopic 3D surface measurement

Autostereoscopic 3D measuring systems are an accurate, rapid, and portable method for in situ measurements. These systems use a micro -lens array to record 3D information based on the light -field theory. However, the spatial -angularresolution trade-off curtails their performance. Although learning models were developed for super -resolution, the scarcity of data hinders efficient training. To address this issue, a novel, to the best of our knowledge, semi -supervised learning paradigm for angular super -resolution is proposed for data -efficient training, benefiting both autostereoscopic and light -field devices. A convolutional neural network using motion estimation is developed for a view synthesis. Subsequently, a high -angular -resolution autostereoscopic system is presented for an accurate profile reconstruction. Experiments show that the semi -supervision enhances view reconstruction quality, while the amount of training data required is reduced by over 69%. (c) 2024 Optica Publishing Group