GPU Based Implementation of 3DTV System

Abstract

This paper focuses on the near real-time implementation of end-to-end 3DTV System. It is specially designed for the generation of high-quality disparity map and depth-image-based rendering (DIBR) on the graphics processing unit (GPU) through CUDA (Compute Unified Device Architecture) API. We propose our novel methods including a kind of stereo matching with adaptive windows and an asymmetric edge adaptive filter (AEAF) for industrial application. These algorithms are structured in a way that exposes as much data parallelism as possible and the power of shared memory and data parallel programming in GPU is exploited. We evaluate our proposed methods and implementation based on the benchmark Middlebury and the experiment results show that our method is suitable for application on the trade-off among accuracy and execution speed. Running on an NVIDIA Quadro FX4800 graphics card, for each 480x375 stereo images with 60 disparity levels, the proposed system reaches about 146ms for stereo matching and reaches the speed of DIBR 5.7ms for rendering 1 view or 14ms for rendering 8 views.