  Real-world 3D data may contain intricate details defined by salient surface
gaps. Automated reconstruction of these open surfaces (e.g., non-watertight
meshes) is a challenging problem for environment synthesis in mixed reality
applications. Current learning-based implicit techniques can achieve high
fidelity on closed-surface reconstruction. However, their dependence on the
distinction between the inside and outside of a surface makes them incapable of
reconstructing open surfaces. Recently, a new class of implicit functions have
shown promise in reconstructing open surfaces by regressing an unsigned
distance field. Yet, these methods rely on a discretized representation of the
raw data, which loses important surface details and can lead to outliers in the
reconstruction. We propose IPVNet, a learning-based implicit model that
predicts the unsigned distance between a surface and a query point in 3D space
by leveraging both raw point cloud data and its discretized voxel counterpart.
Experiments on synthetic and real-world public datasets demonstrates that
IPVNet outperforms the state of the art while producing far fewer outliers in
the reconstruction.
