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.

在 Matterport 3D 等实景环境中，通过实例化大规模导航任务，我们研究了利用 3D 点云或 RGB 图像或它们的组合进行导航的方法，发现前进或随机这两种相对简单的导航方法强且难以超越，同时点云对于学习避免障碍提供了更丰富的信号。我们发现一种称为变曲点权重技术对于用于行为克隆的循环模型训练导航非常重要，并且能够使用此技术胜过基线。这一结果为继续研究基于 3D 深度学习模型的体式导航提供了动力。