Light Field-Based Underwater 3D Reconstruction via Angular Re-Sampling

Yuqi Ding, Zhang Chen, Yu Ji, Jingyi Yu, Jinwei Ye

Abstract: Recovering 3D geometry of underwater scenes is challenging because of the non-linear refraction of light at the water-air interface, which is caused by the camera housing. We present a light field-based approach that leverages properties of angular samples for high-quality underwater 3D reconstruction from a single viewpoint. Specifically, we re-sample the light field image to angular patches. As underwater scenes exhibit weak view-dependent specularity, an angular patch tends to have uniform intensity when sampled at the correct depth. We thus impose this angular uniformity as a constraint for depth estimation. For efficient angular re-sampling, we design a fast approximation algorithm based on multivariate polynomial regression to approximate nonlinear refraction paths. We further develop a light field calibration algorithm that estimates the water-air interface geometry along with the camera parameters. Comprehensive experiments on synthetic and real data show our method produces state-of-the-art reconstruction of static and dynamic underwater scenes.