Abstract
Surface-based geodesic topology provides strong cues
for object semantic analysis and geometric modeling. However, such connectivity information is lost in point clouds.
Thus we introduce GeoNet, the first deep learning architecture trained to model the intrinsic structure of surfaces
represented as point clouds. To demonstrate the applicability of learned geodesic-aware representations, we propose fusion schemes which use GeoNet in conjunction with
other baseline or backbone networks, such as PU-Net and
PointNet++, for down-stream point cloud analysis. Our
method improves the state-of-the-art on multiple representative tasks that can benefit from understandings of the underlying surface topology, including point upsampling, normal estimation, mesh reconstruction and non-rigid shape
classification.