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We present a fast and accurate method for reconstructing surfaces of revolution (SoR) on 3D data and its application to structural modeling of a cluttered scene in real-time. To estimate a SoR axis, we derive an approximately linear cost function for fast convergence. Also, we design a framework for reconstructing SoR on dense SLAM. In the experiment results, we show our method is accurate, robust...
We present a fast registration framework with estimating surface normals from depth images. The key component in the framework is to utilize adjacent pixels and compute the normal at each pixel on a depth image by following three steps. First, image gradients on a depth image are computed with a 2D differential filtering. Next, two 3D gradient vectors are computed from horizontal and vertical depth...
Detection of moving objects is one of the key steps for vision based applications. Many previous works leverage background subtraction using background models and assume that image sequences are captured from a stationary camera. These methods are not directly applied to image sequences from a moving camera because both foreground and background objects move with respect to the camera. One of the...
This paper presents a method named Depth-Assisted Rectification of Contours (DARC) for detection and pose estimation of texture-less planar objects using RGB-D cameras. It consists in matching contours extracted from the current image to previously acquired template contours. In order to achieve invariance to rotation, scale and perspective distortions, a rectified representation of the contours is...
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