论文标题
非常稀疏的数据深度完成深度：调查
Deep Depth Completion from Extremely Sparse Data: A Survey
论文作者
论文摘要
深度完成的目的是从深度传感器（例如Lidars）中捕获的极稀疏的地图中预测密集的像素深度。它在各种应用中起着至关重要的作用，例如自动驾驶，3D重建，增强现实和机器人导航。基于深度学习的解决方案已经证明了这项任务的最新成功。在本文中，我们首次提供了全面的文献综述，可以帮助读者更好地掌握研究趋势并清楚地了解当前的进步。我们通过通过对现有方法进行分类的新型分类法提出的建议，研究网络体系结构，损失功能，基准数据集的设计方面，基准数据集和学习策略的相关研究。此外，我们对三个广泛使用的基准（包括室内和室外数据集）上的模型性能进行了定量比较。最后，我们讨论了先前作品的挑战，并为读者提供一些有关未来研究方向的见解。
Depth completion aims at predicting dense pixel-wise depth from an extremely sparse map captured from a depth sensor, e.g., LiDARs. It plays an essential role in various applications such as autonomous driving, 3D reconstruction, augmented reality, and robot navigation. Recent successes on the task have been demonstrated and dominated by deep learning based solutions. In this article, for the first time, we provide a comprehensive literature review that helps readers better grasp the research trends and clearly understand the current advances. We investigate the related studies from the design aspects of network architectures, loss functions, benchmark datasets, and learning strategies with a proposal of a novel taxonomy that categorizes existing methods. Besides, we present a quantitative comparison of model performance on three widely used benchmarks, including indoor and outdoor datasets. Finally, we discuss the challenges of prior works and provide readers with some insights for future research directions.
