  To help bridge the gap between internet vision-style problems and the goal of
vision for embodied perception we instantiate a large-scale navigation task --
Embodied Question Answering [1] in photo-realistic environments (Matterport
3D). We thoroughly study navigation policies that utilize 3D point clouds, RGB
images, or their combination. Our analysis of these models reveals several key
findings. We find that two seemingly naive navigation baselines, forward-only
and random, are strong navigators and challenging to outperform, due to the
specific choice of the evaluation setting presented by [1]. We find a novel
loss-weighting scheme we call Inflection Weighting to be important when
training recurrent models for navigation with behavior cloning and are able to
out perform the baselines with this technique. We find that point clouds
provide a richer signal than RGB images for learning obstacle avoidance,
motivating the use (and continued study) of 3D deep learning models for
embodied navigation.
