基于KNN搜索的ToF深度图引导被动立体

Liwen Liu, Lianghao Wang
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引用次数: 2

摘要

被动立体相机和飞行时间相机(ToF)都可以为拍摄的真实场景提供深度图,但它们都有先天的局限性。由于ToF相机和被动立体在某些场景中本质上是互补的,因此需要适当地利用ToF相机和被动立体的所有可用信息。目前提出的融合方法没有考虑ToF可靠性检测和基于ToF的被动立体改进。因此,本文提出了一种将ToF相机与无源立体相结合的方法,以获得高精度的深度图。主要贡献是:设计了能量代价函数,利用ToF相机的深度图来指导被动立体匹配。实验结果表明,该方法具有较高的精度和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ToF depth map guided passive stereo based on KNN searching
Both passive stereo and Time-of-Flight (ToF) cameras can provide the depth map for the captured real scenes, but they both have innate limitations. Since ToF cameras and passive stereo are intrinsically complementary for certain scenes, it is desirable to appropriately leverage all the available information by ToF cameras and passive stereo. The fusion methods which are presented recently fail to consider ToF reliability detection and ToF based improvement of passive stereo. As a result, this paper proposes an approach to integrate ToF cameras and passive stereo to obtain high accuracy depth maps. The main contribution is: an energy cost function is designed to utilize depth map from ToF cameras to guide the stereo matching of passive stereo. The experiments show that the proposed approach achieves the improved results with high accuracy and robustness.
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