  We introduce anchored radial observations (ARO), a novel shape encoding for
learning implicit field representation of 3D shapes that is category-agnostic
and generalizable amid significant shape variations. The main idea behind our
work is to reason about shapes through partial observations from a set of
viewpoints, called anchors. We develop a general and unified shape
representation by employing a fixed set of anchors, via Fibonacci sampling, and
designing a coordinate-based deep neural network to predict the occupancy value
of a query point in space. Differently from prior neural implicit models that
use global shape feature, our shape encoder operates on contextual,
query-specific features. To predict point occupancy, locally observed shape
information from the perspective of the anchors surrounding the input query
point are encoded and aggregated through an attention module, before implicit
decoding is performed. We demonstrate the quality and generality of our
network, coined ARO-Net, on surface reconstruction from sparse point clouds,
with tests on novel and unseen object categories, "one-shape" training, and
comparisons to state-of-the-art neural and classical methods for reconstruction
and tessellation.
