  Object goal navigation (ObjectNav) in unseen environments is a fundamental
task for Embodied AI. Agents in existing works learn ObjectNav policies based
on 2D maps, scene graphs, or image sequences. Considering this task happens in
3D space, a 3D-aware agent can advance its ObjectNav capability via learning
from fine-grained spatial information. However, leveraging 3D scene
representation can be prohibitively unpractical for policy learning in this
floor-level task, due to low sample efficiency and expensive computational
cost. In this work, we propose a framework for the challenging 3D-aware
ObjectNav based on two straightforward sub-policies. The two sub-polices,
namely corner-guided exploration policy and category-aware identification
policy, simultaneously perform by utilizing online fused 3D points as
observation. Through extensive experiments, we show that this framework can
dramatically improve the performance in ObjectNav through learning from 3D
scene representation. Our framework achieves the best performance among all
modular-based methods on the Matterport3D and Gibson datasets, while requiring
(up to 30x) less computational cost for training.
