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：深度估计是构建移动自主系统时需要解决的关键任务之一。尽管近年来单目深度估计方法有所改进，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，能够提供更准确、更可靠的深度图。然而，当前方法是针对单个LiDAR传感器专门训练的。由于不同传感器的扫描模式存在差异，每使用一种新传感器都需要重新训练专用的深度补全模型，这不仅计算效率低下，而且缺乏灵活性。因此，我们提出动态调整深度补全模型以适应所使用传感器类型的方法，从而实现LiDAR自适应深度补全。具体而言，我们提出了一种元深度补全网络，该网络使用从数据中提取的数据模式来学习一个任务网络，通过调整主深度补全网络的权重，有效解决给定的深度补全任务。该方法展示了在多种LiDAR扫描模式上的强大能力，并且能够泛化到训练过程中未见过的扫描模式。在使用单一模型的情况下，我们的方法比在不同LiDAR模式上训练的非自适应基线方法取得了显著更好的结果。在极其稀疏的情况下，它甚至优于特定于LiDAR的专家模型。这些优势使得单个深度补全模型能够灵活部署在不同传感器上，对于处理采用自适应而非固定扫描模式的新兴LiDAR技术的输入数据，也可能具有重要价值。