Fine-Scale Surface Normal Estimation Using a Single NIR Image

摘要

We present surface normal estimation using a single near infrared (NIR) image. We are focusing on reconstructing fine-scale surface geometry using an image captured with an uncalibrated light source. To tackle this ill-posed problem, we adopt a generative adversarial network, which is effective in recovering sharp outputs essential for fine-scale surface normal estimation. We incorporate the angular error and an inte-grability constraint into the objective function of the network to make the estimated normals incorporate physical characteristics. We train and validate our network on a recent NIR dataset, and also evaluate the generality of our trained model by using new external datasets that are captured with a different camera under different environments.