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Active Image-based Modeling with a Toy Drone

机译:基于活动图像的模型与玩具无人机建模

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Image-based modeling techniques [1], [2], [3] can now generate photo-realistic 3D models from images. But it is up to users to provide high quality images with good coverage and view overlap, which makes the data capturing process tedious and time consuming. We seek to automate data capturing for image-based modeling. The core of our system is an iterative linear method to solve the multi-view stereo (MVS) problem quickly and plan the Next-Best-View (NBV) effectively. Our fast MVS algorithm enables online model reconstruction and quality assessment to determine the NBVs on the fly. We test our system with a toy unmanned aerial vehicle (UAV) in simulated, indoor and outdoor experiments. Results show that our system improves the efficiency of data acquisition and ensures the completeness of the final model.
机译:基于图像的建模技术[1],[2],[3]现在可以从图像生成照片逼真的3D模型。但是,用户可以提供具有良好覆盖和视图重叠的高质量图像,这使得数据捕获过程繁琐且耗时。我们寻求自动捕获基于图像的建模的数据。我们的系统的核心是一种迭代的线性方法,可以快速解决多视图立体声(MVS)问题并有效地规划下一个最佳视图(NBV)。我们的快速MVS算法使在线模型重建和质量评估能够在飞行中确定NBV。我们使用模拟,室内和室外实验中的玩具无人驾驶飞行器(UAV)测试我们的系统。结果表明,我们的系统提高了数据采集的效率,并确保了最终模型的完整性。

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