GenS: Generalizable Neural Surface Reconstruction from Multi-View Images (Supplemental material) A Implementation details of the network

The detailed network architecture is shown in Tab. 1. "Out" As shown in Tab. 2, we inject cost Here, we show more ablation studies in dense setting. C.1 Generalized multi-scale volume GMV MFS VCL Mean 1.92 1.08 0.83 0.81 The "Base" in Tab. 5 is a model with only the generalized The "PC" stands for the model applying The results show that it cannot work well for generalization training. Based on this intuition, we attempt to increase the receptive field of image patches, that is, we downsample the image in the early stage, and then sample the image patch for multi-view matching. We call this strategy multi-scale photometric consistency (MPC). Tab. 5 show that enlarging the receptive field works well for our generalization training and brings FPN feature network to achieve our multi-scale feature-metric consistency, which simultaneously have different ranges of receptive fields.