ARO-NET

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... Visually, ARO-Net produces the most complete, artifact-free results with faithful reconstruction of fine details. In this airplane case, ARO-Net managed to reconstruct the left-side turbine from extremely sparse points, while other methods all completely missed it... In practice, shapes can only be partially observed by a set of anchors. We introduce ARO-Net to predict the occupancy from less/incomplete Information after training with data. ARO-Net learns to predict the occupancy of a query point given only partial observations.