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首页> 外文期刊>ISPRS Journal of Photogrammetry and Remote Sensing >Fast robust large-scale mapping from video and internet photo collections
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Fast robust large-scale mapping from video and internet photo collections

机译:从视频和互联网照片集快速强大的大规模映射

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摘要

This paper presents a system approaching fully automatic 3D modeling of large-scale environments. Our system takes as input either a video stream or collection of photographs obtained from Internet photo sharing web-sites such as Flickr. The system achieves high computational performance through algorithmic optimizations for efficient robust estimation, the use of image-based recognition for efficient grouping of similar images, and two-stage stereo estimation for video streams that reduces the computational cost while maintaining competitive modeling results. In addition to algorithmic advances, we achieve a major improvement in computational speed through parallelization and execution on commodity graphics hardware. These improvements lead to real-time video processing and to reconstruction from tens of thousands of images within the span of a day on a single commodity computer. We demonstrate modeling results on a variety of real-world video sequences and photo collections.
机译:本文提出了一种对大型环境进行全自动3D建模的系统。我们的系统将视频流或从Internet照片共享网站(例如Flickr)获得的照片集作为输入。该系统通过对算法进行优化以实现有效的鲁棒性估计,使用基于图像的识别来对相似图像进行有效分组,以及对视频流进行两阶段立体声估计,从而在保持竞争建模结果的同时降低了计算成本,从而实现了高计算性能。除了算法方面的进步外,我们还通过在商品图形硬件上并行化和执行,在计算速度上实现了重大改进。这些改进可以实现实时视频处理,并在一天内通过一台商用计算机从数以万计的图像中重建图像。我们在各种现实世界的视频序列和照片集上演示建模结果。

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