Depth perception models are typically trained on non-interactive datasets
with predefined camera trajectories. However, this often introduces systematic
biases into the learning process correlated to specific camera paths chosen
during data acquisition. In this paper, we investigate the role of how data is
collected for learning depth completion, from a robot navigation perspective,
by leveraging 3D interactive environments. First, we evaluate four depth
completion models trained on data collected using conventional navigation
techniques. Our key insight is that existing exploration paradigms do not
necessarily provide task-specific data points to achieve competent unsupervised
depth completion learning. We then find that data collected with respect to
photometric reconstruction has a direct positive influence on model
performance. As a result, we develop an active, task-informed, depth
uncertainty-based motion planning approach for learning depth completion, which
we call DEpth Uncertainty-guided eXploration (DEUX). Training with data
collected by our approach improves depth completion by an average greater than
18% across four depth completion models compared to existing exploration
methods on the MP3D test set. We show that our approach further improves
zero-shot generalization, while offering new insights into integrating robot
learning-based depth estimation.

通过对机器人导航视角下利用三维交互环境收集数据进行深度完成学习的研究，我们评估了四个深度完成模型，并发现数据的采集方式对模型性能具有直接的积极影响。因此，我们提出了一种基于深度不确定性的主动任务导向的深度完成学习方法（DEUX），通过使用我们的方法收集的数据，相比于现有的探索方法，平均提高了超过 18％的深度完成模型在 MP3D 测试集上的性能，同时对零样本泛化性能有所改善，并提供了整合基于机器人学习的深度估计的新见解。