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.

本文采用最大后验概率方法将低分辨率深度数据与高分辨率立体数据相结合，通过层次求解一系列局部能量最小化问题来产生深度图。通过新的相关函数、自适应成本聚合步骤以及基于场景纹理和摄像机几何的自适应融合方法，提出了一种精度更高的种子生长方法，避免了传播错误视差的倾向。该方法是高效的，可在标准台式计算机上以 3 FPS 的速度运行，并通过与现有方法的定量比较和实际深度 - 立体数据集的定性比较，证明了其强大的性能。