Abstract
Conventional computer vision algorithms, including stereo matching algorithms, take finely rendered color images as input. However, existing image signal processing (ISP) pipelines for color image generation are designed for photography with a goal of generating pleasing images for human eyes. This paper describes a new end-to-end pipeline for stereo matching from raw Bayer pattern images to disparity maps with customized ISP. Unlike conventional stereo matching systems which need a complete ISP module to render full-size standard RGB (sRGB) images, a subsampling-based demosaicing-downsampling (SDD) operation is introduced in the proposed pipeline to demosaic and downsample the Bayer pattern images. The resultant half-size color image pairs are processed with simple denoising and tone mapping algorithms to generate the final input images of stereo matching algorithms. It is found that the simple nearest neighbor upsampling method is good enough to generate the final full-size disparity maps. Experimental results show that the proposed pipeline is capable of generating comparable or even better stereo matching results than the conventional pipeline. By skipping most of the unnecessary ISP steps and reducing the size of input images, the computational complexity of the end-to-end stereo matching pipeline is significantly reduced.
Highlights
Stereo vision has received great attention over the past several decades
EXPERIMENTS In the experiments, three well-known stereo matching algorithms, which are a) guided filter (GF) [3], b) seim-global block matching (SGBM) that implemented in OpenCV and c) LocalExp [9] are used as benchmark algorithms to demonstrate the effectiveness of the proposed stereo matching pipeline from raw Bayer pattern images to disparity maps
Since stereo matching are performed on half-size input pairs and most of the image signal processing (ISP) steps are skipped, the computational complexity of the proposed stereo matching pipeline is significantly less than that of the conventional pipeline
Summary
Stereo vision has received great attention over the past several decades. It is widely applied in various applications such as autonomous driving, robots and navigation. S. Gao et al.: Low-Complexity End-to-End Stereo Matching Pipeline From Raw Bayer Pattern Images to Disparity Maps. We propose an end-to-end pipeline for stereo matching from Bayer pattern images to disparity maps. As many of the stereo matching algorithms utilize the color information of images, it is reasonable to convert singlechannel raw Bayer images to three-channel RGB images It is well-known that modern demosaicing algorithms are computation intensive [15]. As will be shown in the experiments, the proposed end-to-end stereo matching pipeline is able to generate comparable or even better matching results than the conventional pipeline, even if it skips many steps in conventional ISP.
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