  During 3D reconstruction, it is often the case that people cannot scan each
individual object from all views, resulting in missing geometry in the captured
scan. This missing geometry can be fundamentally limiting for many
applications, e.g., a robot needs to know the unseen geometry to perform a
precise grasp on an object. Thus, we introduce the task of semantic instance
completion: from an incomplete RGB-D scan of a scene, we aim to detect the
individual object instances and infer their complete object geometry. This will
open up new possibilities for interactions with objects in a scene, for
instance for virtual or robotic agents. We tackle this problem by introducing
RevealNet, a new data-driven approach that jointly detects object instances and
predicts their complete geometry. This enables a semantically meaningful
decomposition of a scanned scene into individual, complete 3D objects,
including hidden and unobserved object parts. RevealNet is an end-to-end 3D
neural network architecture that leverages joint color and geometry feature
learning. The fully-convolutional nature of our 3D network enables efficient
inference of semantic instance completion for 3D scans at scale of large indoor
environments in a single forward pass. We show that predicting complete object
geometry improves both 3D detection and instance segmentation performance. We
evaluate on both real and synthetic scan benchmark data for the new task, where
we outperform state-of-the-art approaches by over 15 in<EMAIL_ADDRESS>on ScanNet, and
over 18 in<EMAIL_ADDRESS>on SUNCG.
