A Real-Time Low-Power Stereo Vision Engine Using Semi-Global Matching

Stefan K. Gehrig, Felix Eberli, Thomas Meyer


Many real-time stereo vision systems are available on low-power platforms. They all either use a local correlation-like stereo engine or perform dynamic programming variants on a scan-line. However, when looking at high-performance global stereo methods as listed in the upper third of the Middlebury database, the low-power real-time implementations for these methods are still missing. We propose a real-time implementation of the semi-global matching algorithm with algorithmic extensions for automotive applications on a reconfigurable hardware platform resulting in a low power consumption of under 3W. The algorithm runs at 25Hz processing image pairs of size 750x480 pixels and computing stereo on a 680x400 image part with up to a maximum of 128 disparities.

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