Light Field Depth Estimation on Off-the-Shelf Mobile GPU

Andre Ivan, Williem, In Kyu Park; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2018, pp. 634-643

While novel light processing algorithms have been continuously introduced, it is still challenging to perform light field processing on a mobile device with limited computation resource due to the high dimensionality of light field data. Recently, the performance of mobile graphics processing unit (GPU) increases rapidly and GPGPU on mobile GPU utilizes massive parallel computation to solve various computer vision problems with high computational complexity. To show the potential capability of light field processing on mobile GPU, we parallelize and optimize the state-of-the-art light field depth estimation which is essential to many light field applications. We employ both algorithm and kernel-based optimization to enable light field processing on mobile GPU. Light field processing involves independent pixel processing with intensive floating-point operations that can be vectorized to match single instruction multiple data (SIMD) style of GPU architecture. We design efficient memory access, caching, and prefetching to exploit light field properties. The experimental result shows that the light field depth estimation on mobile GPU obtains comparable performance as on the desktop CPU. The proposed optimization method gains up to 25 times speedup compared to the naive baseline method.