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On reconstruction of 3-D volumetric models of reefs and benthic structures from image sequences of a stereo rig

机译:从立体钻机的图像序列重建珊瑚礁和底栖结构的3-D体积模型

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Acquiring 3-D computer models of objects from images has been an active computer vision research topic. A common approach uses overlapping images from different views around the object, and attempts to fuse all the information to construct the model by volume intersection. Most current methods have in common that the camera poses for these views are known. To achieve this, a monocular camera circles the target object on a robot arm, or equivalently, views a target on a rotating platform (e.g., a turntable), while the images are acquired at known intervals. We are interested in the deployment of a submersible platform equipped with stereo vision to construct 3-D volumetric models of benthic habitat objects, e.g., coral reefs and other seafloor structures. In this application, we would automatically acquire continuous 360-degrees stereo views of object(s) of interest, by processing the images online to estimate and control camera trajectory and views. In particular, we show that the redundancy from binocular cues improves the robustness and accuracy. Upon completion of the image data acquisition, a bundle adjustment formulation is proposed to recompute the trajectory and the stereo rig poses, as well as to generate object model(s).
机译:从图像获取对象的3D计算机模型一直是计算机视觉研究的活跃话题。一种常见的方法是使用来自对象周围不同视图的重叠图像,并尝试融合所有信息以通过体积相交构造模型。当前大多数方法的共同点是,这些视图的相机姿势是已知的。为了实现这一点,单眼相机在机械臂上环绕目标对象,或者等效地,在旋转平台(例如,转盘)上观看目标,同时以已知间隔获取图像。我们对配备立体视觉的潜水平台的部署感兴趣,以构建底栖栖息地物体(例如珊瑚礁和其他海底结构)的3-D体积模型。在此应用程序中,我们将通过在线处理图像以估计和控制相机的轨迹和视图,自动获取感兴趣对象的连续360度立体视图。特别是,我们证明了来自双目线索的冗余提高了鲁棒性和准确性。在完成图像数据采集后,提出了束调整公式,以重新计算轨迹和立体台架姿势,并生成对象模型。

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