论文标题
实时深度处理的单个存储半全球匹配
Single Storage Semi-Global Matching for Real Time Depth Processing
论文作者
论文摘要
深度图是计算机视觉和机器人技术中的关键计算。最受欢迎的方法之一是通过计算从立体声摄像机获得的图像的差异图。半全球匹配（SGM）方法是一个流行的选择，可以在合理的计算时间内进行良好的准确性。要将这种计算密集型算法用于实时应用，例如用于自动航空车，盲目辅助等。使用GPU加速，FPGA是必要的。在本文中，我们显示了立体视觉系统的设计和实现，该系统基于更全球匹配（MGM）的FPGA实施。米高梅是SGM的变体。我们使用4个路径，但为相应的像素存储一个累积成本值。我们的立体视频原型使用包含基于ARM的Zynq-Soc，Zed-stereo-Camera / elp立体声相机 / Intel Realsense D435I和VGA的ZEDBOARD和VGA进行可视化。归因于深度图所需的基于FPGA的自定义差异图计算加速度的功耗仅为0.72瓦。差异图的更新速率是现实的10.5 fps。
Depth-map is the key computation in computer vision and robotics. One of the most popular approach is via computation of disparity-map of images obtained from Stereo Camera. Semi Global Matching (SGM) method is a popular choice for good accuracy with reasonable computation time. To use such compute-intensive algorithms for real-time applications such as for autonomous aerial vehicles, blind Aid, etc. acceleration using GPU, FPGA is necessary. In this paper, we show the design and implementation of a stereo-vision system, which is based on FPGA-implementation of More Global Matching(MGM). MGM is a variant of SGM. We use 4 paths but store a single cumulative cost value for a corresponding pixel. Our stereo-vision prototype uses Zedboard containing an ARM-based Zynq-SoC, ZED-stereo-camera / ELP stereo-camera / Intel RealSense D435i, and VGA for visualization. The power consumption attributed to the custom FPGA-based acceleration of disparity map computation required for depth-map is just 0.72 watt. The update rate of the disparity map is realistic 10.5 fps.
