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首页> 外文期刊>Journal of visual communication & image representation >Efficient hundreds-baseline stereo by counting interest points for moving omni-directional multi-camera system
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Efficient hundreds-baseline stereo by counting interest points for moving omni-directional multi-camera system

机译：通过计算兴趣点，为移动全向多摄像机系统提供高效的数百个基准立体声

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In this article, we propose an efficient method for estimating a depth map from long-baseline image sequences captured by a calibrated moving multi-camera system. Our concept for estimating a depth map is very simple; we integrate the counting of the total number of interest points (TNIP) in images with the original framework of multiple baseline stereo. Even by using a simple algorithm, the depth can be determined without computing similarity measures such as SSD (sum of squared differences) and NCC (normalized cross correlation) that have been used for conventional stereo matching. The proposed stereo algorithm is computationally efficient and robust for distortions and occlusions and has high affinity with omni-directional and multi-camera imaging. Although expected trade-off between accuracy and efficiency is confirmed for a naive TNIP-based method, a hybrid approach that uses both TNIP and SSD improve this with realizing high accurate and efficient depth estimation. We have experimentally verified the validity and feasibility of the TNIP-based stereo algorithm for both synthetic and real outdoor scenes.
机译：在本文中，我们提出了一种有效的方法，用于从由校准的移动多摄像机系统捕获的长基线图像序列估计深度图。我们估计深度图的概念非常简单。我们将图像中的兴趣点总数（TNIP）的计数与多个基线立体图像的原始框架相结合。即使使用简单的算法，也可以确定深度，而无需计算已用于常规立体声匹配的相似性度量，例如SSD（平方差之和）和NCC（归一化互相关）。所提出的立体算法对于失真和遮挡具有计算效率和鲁棒性，并且与全向和多摄像机成像具有很高的亲和力。尽管对于基于TNIP的简单方法已经确认了精度和效率之间的预期折衷，但是同时使用TNIP和SSD的混合方法通过实现高精度和高效的深度估计改善了这一点。我们已经通过实验验证了基于TNIP的立体声算法在合成和真实室外场景中的有效性和可行性。

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