  Surface reconstruction from point clouds is a fundamental problem in the
computer vision and graphics community. Recent state-of-the-arts solve this
problem by individually optimizing each local implicit field during inference.
Without considering the geometric relationships between local fields, they
typically require accurate normals to avoid the sign conflict problem in
overlapped regions of local fields, which severely limits their applicability
to raw scans where surface normals could be unavailable. Although SAL breaks
this limitation via sign-agnostic learning, further works still need to explore
how to extend this technique for local shape modeling. To this end, we propose
to learn implicit surface reconstruction by sign-agnostic optimization of
convolutional occupancy networks, to simultaneously achieve advanced
scalability to large-scale scenes, generality to novel shapes, and
applicability to raw scans in a unified framework. Concretely, we achieve this
goal by a simple yet effective design, which further optimizes the pre-trained
occupancy prediction networks with an unsigned cross-entropy loss during
inference. The learning of occupancy fields is conditioned on convolutional
features from an hourglass network architecture. Extensive experimental
comparisons with previous state-of-the-arts on both object-level and
scene-level datasets demonstrate the superior accuracy of our approach for
surface reconstruction from un-orientated point clouds. The code is available
at https://github.com/tangjiapeng/SA-ConvONet.
