We present IterMVS, a new data-driven method for high-resolution multi-view stereo. We propose a novel GRU-based estimator that encodes pixel-wise probability distributions of depth in its hidden state. Ingesting multi-scale matching information, our model refines these distributions over multiple iterations and infers depth and confidence. To extract the depth maps, we combine traditional classification and regression in a novel manner. We verify the efficiency and effectiveness of our method on DTU, Tanks&Temples and ETH3D. While being the most efficient method in both memory and run-time, our model achieves competitive performance on DTU and better generalization ability on Tanks&Temples as well as ETH3D than most state-of-the-art methods. Code is available at https://github.com/FangjinhuaWang/IterMVS.
翻译:我们展示了以数据驱动的新高分辨率多视立体驱动的 IterMVS 。 我们提议了一个新的基于 GRU 的测算器,该测算器将隐藏状态中的深度概率分布编码为像素误差。 输入多尺度的匹配信息,我们的模型将这些分布精细化为多个迭代和推断深度和信任度。 为了提取深度地图, 我们以新颖的方式将传统分类和回归结合起来。 我们验证了我们关于DTU、 Tanks & Temples 和 ETH3D 的方法的效率和效力。 我们的模式虽然在记忆和运行时段都是最有效的方法,但是在DTU上取得了竞争性的性能,并且比大多数最先进的方法在Tanks & Temples和ETH3D上取得了更好的通用能力。 代码可以在 https://github.com/FangjinhuWang/IterMVSS上查阅。