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.