Stereo Vision Algorithms Suited to Constrained FPGA Cameras

Abstract

AbstractThe advent of cheap RGBD active 3D sensors, such as those based on structured light (e.g., the Microsoft Kinect) or those based on time-of-flight technology, has significantly increased the interest in computer vision applications based on depth data that, in most cases, enables higher robustness compared to solutions based on traditional 2D images. Unfortunately, active techniques are quite noisy or even completely useless in outdoor environments (in particular under sunlight). An effective and well-known technique to infer depth suited to indoor and outdoor environments is passive stereo vision. Nevertheless, despite the frequent deployment of this technology in many research projects since the 1960s, stereo vision is often perceived, especially in consumer applications, as an expensive technology due to its high demanding computation requirements. In this paper, we will review a subset of state-of-the-art stereo vision algorithms that have the potential to fit with a basic computing architecture made of a low-cost field-programmable gate arrays (FPGAs), without additional external devices (e.g., FIFOs, DDR memories, etc.) excluding a USB or GigaEthernet communication controller. Compared to more complex designs based on expensive FPGAs coupled with additional external memory devices, clear advantages of the outlined simplified computing architecture are the reduced design and manufacturing costs as well as the reduced power consumption. Another significant advantage consists in better code portability as well as in improved robustness with respect to obsolescence of electronic devices being almost the whole design self-contained into the FPGA logic. On the other hand, mapping stereo vision algorithms into a similar low-power, low-cost architecture poses a very challenging task and only a subset of existing algorithms appropriately modified are suited to this constrained computing platform. Nevertheless, we believe that devices based on such a proposed simplified computing architecture would make RGBD sensors based on stereo vision suitable to a wider class of application scenarios not yet fully addressed by this technology.KeywordsLocal AlgorithmStereo VisionStereo PairComputing ArchitectureCost AggregationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.