我们首先展示提出的R-MVSNet的SOAT性能,其表现可以比拟甚至由于先前的MVSNet。 Qualitative :定性的 Quantitative:定量的 DTU 数据集 我们在DTU评估标准上评价提出的方法。为了对比MVSNet和R-MVSNet,我们设置深度最小和最大为425和905,所有场景的深度间隔为256。量化结果在表1中。准确度和完整度的计算采用DTU数据集提供...
R-MVSNet: gather spatial and temporal context information in the depth direction(空间和时间上下文信息) 网络架构Sequential Processingwinner-takes-all: replace the pixel-wise depth value with the best one(noise) spatial: filter the matching cost at different depths, gather spatial context information re...
contribution: scalable MVS framework 内存消耗减少,也可以应用大场景 instead of regularizing the entire 3D cost volume in one go, R-MVSNet sequentially regularizes the 2D cost maps along the depth direction via the gated recurrent unit(GRU) key insight: regularize the cost volume in a sequential ...