Learning to Orient Surfaces by Self-supervised Spherical CNNs (Supplementary Material)

In this section, we study how the data augmentation carried out while training on local surface patches improves the robustness of Compass against self-occlusions and missing parts. Results for 3DMatch are shown in Table 1: the performance gain achieved by Compass when deploying the proposed data augmentation validates its importance. Indeed, without the proposed augmentation FLARE performs better than Compass on this dataset. Compass on 3DMatch and 3DMatch rotated. CNNs, we are able to achieve a similar performance on both datasets.