  Reconstruction of 3D open surfaces (e.g., non-watertight meshes) is an
underexplored area of computer vision. Recent learning-based implicit
techniques have removed previous barriers by enabling reconstruction in
arbitrary resolutions. Yet, such approaches often rely on distinguishing
between the inside and outside of a surface in order to extract a zero level
set when reconstructing the target. In the case of open surfaces, this
distinction often leads to artifacts such as the artificial closing of surface
gaps. However, real-world data may contain intricate details defined by salient
surface gaps. Implicit functions that regress an unsigned distance field have
shown promise in reconstructing such open surfaces. Nonetheless, current
unsigned implicit methods rely on a discretized representation of the raw data.
This not only bounds the learning process to the representation's resolution,
but it also introduces outliers in the reconstruction. To enable accurate
reconstruction of open surfaces without introducing outliers, we propose a
learning-based implicit point-voxel model (IPVNet). IPVNet 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 resulting
reconstruction.
