三维光场显示，因其视角大、分辨率高等特点，受到众多研究人员的青睐。但是，三维光场显示技术依然面临着很多挑战，其中之一就是光场显示中视点数量采集不足的问题。. 因此，本课题将开展大视角三维光场显示中基于无监督深度学习的密集视点生成方法研究，采集稀疏光场视点，合成密集虚拟视点，验证显示三维光场效果。构建大视角光场的最小化离散抽样机理，实现大视角三维光场的稀疏视点采集；提出大视角连续光场的恢复原理，实现大视角三维光场的密集视点生成；利用自主研发的三维光场设备进行显示验证；评估显示结果，优化调整采集及合成中多项参数。.本课题利用无监督深度学习的方法，将采集的稀疏光场视点合成为虚拟的密集光场视点。相比于有监督网络，可以在保证视点质量的前提下，不需要大量的虚拟视点作为训练目标，并且生成的虚拟位置能够灵活调整。本课题的实现将极大的简化光场采集的难度，为光场显示的广泛应用打下基础。

Three-dimensional (3D) light field display, as one of the potential directions of future display technology, has still attracted many researchers' attention, because of its large view-angle, high resolution and real-time rendering. However, 3D light field display still faces many challenges. One of them is the number of viewpoint is insufficient for the light field display..Here, the research of multi-view synthesis for light field display based on unsupervised deep learning is present. The principle of minimum discrete sample of light field is established to achieve sparse views collection. The principle of continuous light field restoration is proposed to achieve dense views synthesis. A self-manufactured light field display is used to verify the displaying result. Based on the performance, parameters of view capture and view synthesis are adjusted and optimized. .The method of unsupervised deep learning is used to synthesize the virtual dense light field viewpoints with sparse light field viewpoints. Compared with the supervised network, it does not decrease the quality of synthesized result, without requiring a large number of training targets, and the virtual location can be flexibly adjusted. The realization of this research will greatly simplify the construction of light field capture, and lay a foundation for the wide application of light field display.