Multi-Cue-Based Circle Detection and Its Application to Robust Extrinsic Calibration of RGB-D Cameras
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TL;DR: This paper presents a multi-cue-based method for detecting circular regions in a single color image and proposes to use robust cost functions to reduce errors due to misdetected sphere centers in extrinsic calibration of multiple RGB-D cameras.
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Abstract: RGB-Depth (RGB-D) cameras are widely used in computer vision and robotics applications such as 3D modeling and human–computer interaction To capture 3D information of an object from different viewpoints simultaneously, we need to use multiple RGB-D cameras To minimize costs, the cameras are often sparsely distributed without shared scene features Due to the advantage of being visible from different viewpoints, spherical objects have been used for extrinsic calibration of widely-separated cameras Assuming that the projected shape of the spherical object is circular, this paper presents a multi-cue-based method for detecting circular regions in a single color image Experimental comparisons with existing methods show that our proposed method accurately detects spherical objects with cluttered backgrounds under different illumination conditions The circle detection method is then applied to extrinsic calibration of multiple RGB-D cameras, for which we propose to use robust cost functions to reduce errors due to misdetected sphere centers Through experiments, we show that the proposed method provides accurate calibration results in the presence of outliers and performs better than a least-squares-based method
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Citations
A fast and accurate circle detection algorithm based on random sampling
TL;DR: Zhang et al. as discussed by the authors proposed a fast and accurate randomized circle detection algorithm, which mainly focuses on four aspects: calculating circle parameters, determining candidate circles, searching for true circle, and improving detection accuracy.
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Iterative K-Closest Point Algorithms for Colored Point Cloud Registration.
TL;DR: Two algorithms are presented to minimize a probabilistic cost based on the color-supported soft matching of points in a point cloud to their K-closest points in the other point cloud, applied to a real-world dataset, providing accurate and visually improved results.
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Robust Extrinsic Calibration of Multiple RGB-D Cameras with Body Tracking and Feature Matching.
TL;DR: In this article, a robust registration method of multiple RGB-D cameras was proposed for human body tracking system provided by Azure Kinect SDK to estimate a coarse global registration between cameras and refine it using feature matching However, the matched feature pairs include mismatches, hindering good performance.
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CNN-Based Denoising, Completion, and Prediction of Whole-Body Human-Depth Images
TL;DR: This paper proposes a learning-based method for reconstructing a whole-body point cloud from a single front-view human-depth image and proposes to use convolutional neural networks that not only predict a back-view depth image but also refine the input front- view depth image.
Colored Point Cloud Registration by Depth Filtering.
Ouk Choi,Wonjun Hwang +1 more
TL;DR: Wang et al. as discussed by the authors proposed a cost function based on an adaptive combination of two different projected distances to prevent numerical instability. But, the cost function is not suitable for color point clouds.
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