Efficient depth propagation in videos with GPU-acceleration

In this paper we propose an optimized semiautomatic approach for efficient 2D-to-3D video conversion. It is based on a conversion algorithm that leverages segmentation and filtering techniques to propagate sparse depth information that was provided by a user. Our GPU acceleration of in the work of Brosch et al. (2011) significantly reduces the computation time of the original algorithm. Since the limited capacity of the CPU´s onboard memory hinders the parallel execution of large data such as videos, we additionally propose a temporally coherent clip-based 2D-to-3D conversion approach for long videos. Evaluations show that the proposed, optimized conversion approach is capable of generating high-quality results, while significantly reducing the execution time compared to the original, un-optimized approach.