Stream-Centric Stereo Matching and View Synthesis: A High-Speed Approach on GPUs

In this paper, we propose a real-time image-based rendering (IBR) system. It is specifically designed for photorealistic view synthesis at high-speed on the graphics processing unit (GPU). We steer the proposed IBR system design with two high-level ideas. First, for cost-effective IBR, as long as the synthesized views look visually plausible, the estimated disparity and occlusion need not be correct. Hence, we jointly optimize stereo matching and view synthesis for a favorable end-to-end performance. Second, for great real-time acceleration on GPUs, all functional modules need be shaped at an early design stage, fitting the massively parallel streaming architecture of GPUs. Based on these two guidelines, we first propose a stream-centric local stereo matching algorithm. The key idea is to construct a versatile set of variable support patterns in a highly efficient manner, and then an optimal local support pattern is selected to approximate varying image structures adaptively. Next, a low-complexity adaptive view synthesis technique is proposed. It efficiently tackles visual artifacts in synthesized images, using a novel photometric outlier detection and handling scheme. We evaluated both the disparity estimation accuracy and novel view synthesis quality of the proposed approach, based on the benchmark Middlebury stereo datasets. The experiments show that our local stereo method produces consistently reliable disparity estimates for both homogeneous regions and depth discontinuities, outperforming several previous GPU-based local methods. More importantly, visually plausible intermediate views are generated by our IBR approach at high-speed on the GPU. With stereo matching and view synthesis completely running on an NVIDIA GeForce 8800 GT graphics card, the proposed IBR system reaches about 100 f/s for 450times375 stereo images with 60 disparity levels.