In this section, we first present an overview of 4 different architectures for neural implicit scene representations anddetails ofMulti-Res. See Figure 1 for an overview over the architectures. More specifically, each grid contains up toT feature vectors with dimensionalityF. We further reportNormal Consistencyfor the Replica dataset following [9,13,18,19,23,32] as near-perfect ground truth is available. We observe that using more input views for training improves reconstruction quality.

Grids in Section 1.1 and provide details of the depth loss In the following, we provide details for Multi-Res. For our single MLP architecture, we use an 8-layer MLP with hidden dimension 256. We use a two-layer MLP with hidden dimension 256 for the SDF prediction for both, Single-Res. For the DTU dataset [1], we follow the official evaluation protocol and report the reconstruction quality with: Accuracy, Completeness and Chamfer Distance . Distance is the mean of Accuracy and Completeness .