This paper addresses the problem of range-stereo fusion, for the construction of high-resolution depth maps. In particular, we combine low-resolution depth data with high-resolution stereo data, in a maximum a posteriori (MAP) formulation. Unlike existing schemes that build on MRF optimizers, we infer the disparity map from a series of local energy minimization problems that are solved hierarchically, by growing sparse initial disparities obtained from the depth data. The accuracy of the method is not compromised, owing to three properties of the data-term in the energy function. Firstly, it incorporates a new correlation function that is capable of providing refined correlations and disparities, via subpixel correction. Secondly, the correlation scores rely on an adaptive cost aggregation step, based on the depth data. Thirdly, the stereo and depth likelihoods are adaptively fused, based on the scene texture and camera geometry. These properties lead to a more selective growing process which, unlike previous seed-growing methods, avoids the tendency to propagate incorrect disparities. The proposed method gives rise to an intrinsically efficient algorithm, which runs at 3FPS on 2.0MP images on a standard desktop computer. The strong performance of the new method is established both by quantitative comparisons with state-of-the-art methods, and by qualitative comparisons using real depth-stereo data-sets.

翻译：本文探讨在建造高分辨率深度地图时的测距聚合问题。特别是,我们将低分辨率深度数据和高分辨率立体数据结合在一起,在最大范围内采用事后(MAP)配方。与以MRF优化器为基础的现有办法不同,我们从一系列本地能源最小化问题中推断出差异图,这些问题是通过从深度数据中获得的分级解决的,从层次上解决的,初始差异越来越少。由于数据周期在能源功能中的三个特性,该方法的准确性没有受到损害。首先,该方法包含一种新的相关功能,能够通过子像素校正提供精细的关联和差异。第二,相关评分取决于基于深度数据的适应性成本汇总步骤。第三,立体和深度的可能性是适应性的,以现场纹理和相机的几何测量为基础。这些特性导致一个更具选择性的增长过程,与以往的种子增长方法不同,避免了传播不正确的差异的趋势。拟议方法产生了一种内在有效的算法,即通过标准台式计算机的2.0MP图像在3PS上进行精确的计算。第二,通过定量方法与定量比较,采用新的方法进行精确的比较。