aro-net.github.io - ARO-Net

Description: Learning Implicit Fields from Anchored Radial Observations.

Example domain paragraphs

ARO-Net encodes contextual information from anchors, which is different against most existing methods (such as Points2Surf and ConvONet ) that encode neighboring information around query point, yielding better reconstruction (see the holes) on sparse input and generalizability on unseen categories.

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 poin

Links to aro-net.github.io (2)

actasidiot.github.io Yizhi Wang
yizhiwang96.github.io Yizhi Wang