Multi-cost loss-based disparity estimation network from light field
Yuanshen An, Xiaojuan Deng, Ligen Shi, Chang Liu, Jun Qiu · 2023
The spatial-angular coupling relationship of light field data is fundamental for the scene-disparity estimation. Occlusion, smoothing and noise are major challenges in disparity estimation of light field. Based on the special geometric structure of bi-plane parameterized light field data, we proposed a novel multi-cost loss light field disparity estimation network. The neural network consists of three modules. Firstly, in order to fully utilize the occlusion information contained in the light field, we divide the light field data into four subsets, and a weight shared network is designed to obtain four initial disparity maps, which embody different occlusion situations. Then, the gradient information of the center view sub-aperture image is used to pick the credible disparity from the initial disparity maps. Lastly, a convolutional neural network is designed to further improve the robustness of the merged disparity map in smooth and noisy regions, while maintaining the structural information of the scene. Experimental results on both synthetic and real datasets show that the proposed method can obtain higher-precision disparity.