Kyungdon Joo

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We present a practical 3D reconstruction method to obtain a high-quality dense depth map from narrow-baseline image sequences captured by commercial digital cameras, such as DSLRs or mobile phones. Depth estimation from small motion has gained interest as a means of various photographic editing, but important limitations present themselves in the form of(More)
Given a set of surface normals, we pose a Manhattan Frame (MF) estimation problem as a consensus set maxi-mization that maximizes the number of inliers over the rotation search space. We solve this problem through a branch-and-bound framework, which mathematically guarantees a globally optimal solution. However, the computational time of conventional(More)
—Most man-made environments, such as urban and indoor scenes, consist of a set of parallel and orthogonal planar structures. These structures are approximated by Manhattan world assumption and be referred to Manhattan Frame (MF). Given a set of inputs such as surface normals or vanishing points, we pose an MF estimation problem as a consensus set(More)
One of the most hazardous driving scenario is the overtaking of a slower vehicle, indeed, in this case the front vehicle (being overtaken) can occlude an important part of the field of view of the rear vehicle's driver. This lack of visibility is the most probable cause of accidents in this context. Recent research works tend to prove that augmented reality(More)
— This paper presents a vision system and a depth processing algorithm for DRC-HUBO+, the winner of the DRC finals 2015. Our system is designed to reliably capture 3D information of a scene and objects robust to challenging environment conditions. We also propose a depth-map upsampling method that produces an outliers-free depth map by explicitly handling(More)
We propose a novel camera calibration method for de-focused images using a smartphone under the assumption that the defocus blur is modeled as a convolution of a sharp image with a Gaussian point spread function (PSF). In contrast to existing calibration approaches which require well-focused images, the proposed method achieves accurate camera calibration(More)
We present a method based on a hierarchical clustering to restore the 3D lines of structured environments. In previous approaches , the restoration of noisy 3D lines is a challenging problem because it is difficult to define a suitable similarity measure discriminative to other lines. Our motivation to overcome the difficulty is that most structured scenes(More)