目的 越来越多的应用依赖于对场景深度图像准确且快速的观测和分析,如机器人导航以及在电影和游戏中对虚拟场景的设计建模等。飞行时间深度相机等直接的深度测量设备可以实时的获取场景的深度图像,但是由于硬件条件的限制,采集的深度图像分辨率比较低,无法满足实际应用的需要。通过立体匹配算法对左右立体图对之间进行匹配获得视差从而得到深度图像是计算机视觉的一种经典方法,但是由于左右图像之间遮挡以及无纹理区域的影响,立体匹配算法在这些区域无法匹配得到正确的视差,导致立体匹配算法在实际应用中存在一定的局限性。方法 结合飞行时间深度相机等直接的深度测量设备和立体匹配算法的优势,提出一种新的深度图像重建方法。首先结合直接的深度测量设备采集的深度图像来构造自适应局部匹配权值,对左右图像之间的局部窗立体匹配过程进行约束,得到基于立体匹配算法的深度图像;然后基于左右检测原理将采集到的深度图像和匹配得到的深度图像进行有效融合;接着提出一种局部权值滤波算法,来进一步提高深度图像的重建质量。结果 实验结果表明,无论在客观指标还是视觉效果上,本文提出的深度图像重建算法较其他立体匹配算法可以得到更好的结果。其中错误率比较实验表明,本文算法较传统的立体匹配算法在深度重建错误率上可以提升10%左右。峰值信噪比实验结果表明,本文算法在峰值信噪比上可以得到10 dB左右的提升。结论 提出的深度图像重建方法通过结合高分辨率左右立体图对和初始的低分辨率深度图像,可以有效地重建高质量高分辨率的深度图像。

Objective The capability to capture depth information of static real-world objects has achieved increased importance in many fields of application, such as manufacturing and prototyping, as well as in the design of virtual worlds for movies and games. A time-of-flight camera can conveniently obtain scene depth images. However, the resolution of a depth image is low and cannot satisfy actual requirements because of hardware limitations. Stereo matching algorithms are classical methods used to obtain depth images, but they are significantly limited in practical applications because of the occlusion between left and right images and the non-textured area. In this study, we propose a novel method to obtain a high-resolution, high-quality depth map by combining stereo matching with the use of a time-of-flight camera.Method We formulate a non-local adaptive weighting filter and obtain an initial high-resolution depth map using the low-resolution depth map from the time-of-flight camera. Then, we use the initial depth map and a local stereo matching algorithm to construct adaptive weights for stereo matching and obtain a raw depth map. Given that discontinuities within a range and coloring tend to coalign, we construct a local weighting filter using the raw depth map and the features of a high-resolution color image to reinforce the preservation of fine details. Result Experiments demonstrate that our approach can obtain an excellent high-resolution range image. Comparison experiments on peak signal-to-noise ratio and error rate show that our method can reconstruct high-quality depth maps. Conclusion The proposed method can produce sharper edges and more accurate details compared with other state-of-the-art approaches.