Scale and Orientation Aware EPI-Patch Learning for Light Field Depth Estimation

Epipolar Plane Image (EPI) implies some important depth cues for light field depth estimation. Intuitively, the EPI patches with different spatial scales and orientations may exhibit different features and result in different estimation precision. In this paper, we discuss this issue and present a scale and orientation aware EPI-Patch learning model for depth estimation. We take the multi-orientation EPI patches of each pixel as input, and design two types of network structures for adaptive scale selection and orientation fusion. One type is a scale-aware structure, which feeds one orientation patch into a multi-layer feed-forward network with long and short skip connections. The other type is a shared-weight network for fusing the multi-orientation features. We demonstrate the effectiveness of our model by experiments on 4D Light Field Benchmark.