Depth estimation is one of the essential tasks to be addressed when creating mobile autonomous systems. While monocular depth estimation methods have improved in recent times, depth completion provides more accurate and reliable depth maps by additionally using sparse depth information from other sensors such as LiDAR. However, current methods are specifically trained for a single LiDAR sensor. As the scanning pattern differs between sensors, every new sensor would require re-training a specialized depth completion model, which is computationally inefficient and not flexible. Therefore, we propose to dynamically adapt the depth completion model to the used sensor type enabling LiDAR adaptive depth completion. Specifically, we propose a meta depth completion network that uses data patterns derived from the data to learn a task network to alter weights of the main depth completion network to solve a given depth completion task effectively. The method demonstrates a strong capability to work on multiple LiDAR scanning patterns and can also generalize to scanning patterns that are unseen during training. While using a single model, our method yields significantly better results than a non-adaptive baseline trained on different LiDAR patterns. It outperforms LiDAR-specific expert models for very sparse cases. These advantages allow flexible deployment of a single depth completion model on different sensors, which could also prove valuable to process the input of nascent LiDAR technology with adaptive instead of fixed scanning patterns.

翻译：深度估计是创建移动自主系统时需解决的关键任务之一。尽管单目深度估计方法近年来有所改进，但深度补全通过额外利用来自其他传感器（如LiDAR）的稀疏深度信息，可提供更精确和可靠的深度图。然而，现有方法需针对单一LiDAR传感器进行专门训练。由于不同传感器的扫描模式存在差异，每更换一种新传感器都需要重新训练专用深度补全模型，这既低效又缺乏灵活性。为此，我们提出动态调整深度补全模型以适应所用传感器类型的方法，实现LiDAR自适应深度补全。具体而言，我们设计了一种元深度补全网络，利用数据衍生出的模式学习任务网络，通过调整主深度补全网络的权重来高效求解给定深度补全任务。该方法展现出处理多种LiDAR扫描模式的强大能力，并能够泛化到训练中未见的扫描模式。在仅使用单一模型的情况下，我们的方法相比于基于不同LiDAR模式训练的非自适应基线方法取得了显著更优的结果，并在极端稀疏情况下优于LiDAR专用专家模型。这些优势使得单一深度补全模型可灵活部署于不同传感器，对于处理采用自适应而非固定扫描模式的新兴LiDAR技术输入也具有重要价值。