This paper presents a real-time processing platform for high-definition stereo video. The system is capable to process stereo video streams at resolutions up to 1, 920 × 1, 080 at 30 frames per second (1080p30). In the hybrid FPGA-GPU-CPU system, a high-density FPGA is used not only to perform the low-level image processing tasks such as color interpolation and cross-image color correction, but also to carry out radial undistortion, image rectification, and disparity estimation. We show how the corresponding algorithms can be implemented very efficiently in programmable hardware, relieving the GPU from the burden of these tasks. Our FPGA implementation results are compared with corresponding GPU implementations and with other implementations reported in the literature.

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Figure 1: Our custom beam-splitter stereo-camera design is comprised of motorized lenses, interaxial distance and convergence. A programmable high performance computational unit controls the motors. User input is performed using a stereoscopic touch screen. AbstractStereoscopic 3D has gained significant importance in the entertainment industry. However, production of high quality stereoscopic content is still a challenging art that requires mastering the complex interplay of human perception, 3D display properties, and artistic intent. In this paper, we present a computational stereo camera system that closes the control loop from capture and analysis to automatic adjustment of physical parameters. Intuitive interaction metaphors are developed that replace cumbersome handling of rig parameters using a touch screen interface with 3D visualization. Our system is designed to make stereoscopic 3D production as easy, intuitive, flexible, and reliable as possible. Captured signals are processed and analyzed in real-time on a stream processor. Stereoscopy and user settings define programmable control functionalities, which are executed in real-time on a control processor. Computational power and flexibility is enabled by a dedicated software and hardware architecture. We show that even traditionally difficult shots can be easily captured using our system.