  We are interested in reconstructing the mesh representation of object
surfaces from point clouds. Surface reconstruction is a prerequisite for
downstream applications such as rendering, collision avoidance for planning,
animation, etc. However, the task is challenging if the input point cloud has a
low resolution, which is common in real-world scenarios (e.g., from LiDAR or
Kinect sensors). Existing learning-based mesh generative methods mostly predict
the surface by first building a shape embedding that is at the whole object
level, a design that causes issues in generating fine-grained details and
generalizing to unseen categories. Instead, we propose to leverage the input
point cloud as much as possible, by only adding connectivity information to
existing points. Particularly, we predict which triplets of points should form
faces. Our key innovation is a surrogate of local connectivity, calculated by
comparing the intrinsic/extrinsic metrics. We learn to predict this surrogate
using a deep point cloud network and then feed it to an efficient
post-processing module for high-quality mesh generation. We demonstrate that
our method can not only preserve details, handle ambiguous structures, but also
possess strong generalizability to unseen categories by experiments on
synthetic and real data. The code is available at
https://github.com/Colin97/Point2Mesh.
