Standard imaging techniques do not get as much information from a scene as light-field imaging. Light-field (LF) cameras can measure the light intensity reflected by an object and, most importantly, the direction of its light rays. This information can be used in different applications, such as depth estimation, in-plane focusing, creating full-focused images, etc. However, standard key-point detectors often employed in computer vision applications cannot be applied directly to plenoptic images due to the nature of raw LF images. This work presents an approach for key-point detection dedicated to plenoptic images. Our method allows using of conventional key-point detector methods. It forces the detection of this key-point in a set of micro-images of the raw LF image. Obtaining this important number of key-points is essential for applications that require finding additional correspondences in the raw space, such as disparity estimation, indirect visual odometry techniques, and others. The approach is set to the test by modifying the Harris key-point detector.
Light-field and plenoptic cameras are widely available today. Compared with monocular cameras, these cameras capture not only the intensity but also the direction of the light rays. Due to this specificity, light-field cameras allow for image refocusing and depth estimation using a single image. However, most of the existing depth estimation methods using light-field cameras require a prior complex calibration phase and raw data preprocessing before the desired algorithm is applied. We propose a homography-based method with plenoptic camera parameters calibration and optimization, dedicated to our homography-based micro-images matching algorithm. The proposed method works on debayerred raw images with vignetting correction. The proposed approach directly links the disparity estimation in the 2D image plane to the depth estimation in the 3D object plane, allowing for direct extraction of the real depth without any intermediate virtual depth estimation phase. Also, calibration parameters used in the depth estimation algorithm are directly estimated, and hence no prior complex calibration is needed. Results are illustrated by performing depth estimation with a focused light-field camera over a large distance range up to 4 m.
Light-Field (LF) cameras allow the extraction not only of the intensity of light but also of the direction of light rays in the scene, hence it records much more information of the scene than a conventional camera. In this paper, we present a novel method to detect key-points in raw LF images by applying key-points detectors on Pseudo-Focused images (PFIs). The main advantage of this method is that we don’t need to use complex key-points detectors dedicated to light-field images. We illustrate the method in two use cases: the extraction of corners in a checkerboard and the key-points matching in two view raw light-field images. These key-points can be used for different applications e.g. calibration, depth estimation or visual odometry. Our experiments showed that our method preserves the accuracy of detection by re-projecting the pixels in the original raw images.
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