论文标题
基于非参数深度分布建模基于多视图立体的深度推断
Non-parametric Depth Distribution Modelling based Depth Inference for Multi-view Stereo
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
Recent cost volume pyramid based deep neural networks have unlocked the potential of efficiently leveraging high-resolution images for depth inference from multi-view stereo. In general, those approaches assume that the depth of each pixel follows a unimodal distribution. Boundary pixels usually follow a multi-modal distribution as they represent different depths; Therefore, the assumption results in an erroneous depth prediction at the coarser level of the cost volume pyramid and can not be corrected in the refinement levels leading to wrong depth predictions. In contrast, we propose constructing the cost volume by non-parametric depth distribution modeling to handle pixels with unimodal and multi-modal distributions. Our approach outputs multiple depth hypotheses at the coarser level to avoid errors in the early stage. As we perform local search around these multiple hypotheses in subsequent levels, our approach does not maintain the rigid depth spatial ordering and, therefore, we introduce a sparse cost aggregation network to derive information within each volume. We evaluate our approach extensively on two benchmark datasets: DTU and Tanks & Temples. Our experimental results show that our model outperforms existing methods by a large margin and achieves superior performance on boundary regions. Code is available at https://github.com/NVlabs/NP-CVP-MVSNet
