A camera-based depth estimation method is essential for self-driving. Depth estimation methods based on stereo cameras capture objects and triangulate the depth between the objects and the camera using two cameras. However, they are vulnerable to parallax distortion owing to vibrations and degradation. On the contrary, depth estimation methods based on a monocular camera, such as depth-from-defocus (DFD), use a focusing mechanism or spectroscopic optics. However, because the sharpness does not change sufficiently in the depth direction, it is not suitable for wide-range estimation. Therefore, we are developing depth-estimation methods using a monocular camera by focusing with tilted optics. When tilted optics is used, the depth of field (DOF) increases toward the depth direction, and the plane of sharp focus (POF) appears tilted. In this study, we propose to improve the accuracy of the depth estimation method using tilted optics with a color filter aperture by performing sharpness calculations corresponding to the edge direction and sharpness model fitting using the weighted least squares method.
We have been investigating a depth estimation system for real-time applications. Stereo camera method is too sensitive to slight variations of baseline length due to vibration and temperature. Additionally, it has occlusion problem. On the other hand, monocular camera method by focusing cannot provide a balance between wide-area estimation and real-time estimation. Therefore, we have proposed a method that adopts tilted lens optics. In this method, the plane of sharp focus (POF) lies and the depth of field (DOF) enlarges toward the depth direction. Herein, we can obtain depth values at each pixel from a ratio of the sharpness values of two tilted optics images using monocular camera system with spectroscopic. This advantage works for real-time application such as automotive tasks. In this paper, we introduce a novel method to realize a compact imaging device which is consist of only one pair of an image sensor and a tilted optics. We have adopted multi-aperture using color filter to achieve our proposal. By using not only a normal aperture but also an aperture with green color filter which is smaller than the normal aperture at the same position in the optics, we realize to obtain two type blur images for tilted depth estimation easily.
We have been investigating a novel depth estimation system that adopts tilted-lens optics for real-time usage, e.g., automotive tasks. Herein, we obtained depth values for each pixel from the sharpness ratio of only two tilted optics images; we used a monocular camera system with a spectroscopic mirror. However, the method causes some estimation errors because of the difference between the optical theory and the actual camera system. Therefore, to reduce the error, we adopted a neural network to obtain the depth map. In this paper, we report our improvement by optimizing the neural network construction which calculates the depth value for each pixel from 3 × 3 pixel values at each image and y-coordinate.
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