This paper presents a novel method for depth completion, which leverages multi-view improved monitored distillation to generate more precise depth maps. Our approach builds upon the state-of-the-art ensemble distillation method, in which we introduce a stereo-based model as a teacher model to improve the accuracy of the student model for depth completion. By minimizing the reconstruction error for a given image during ensemble distillation, we can avoid learning inherent error modes of completion-based teachers. To provide self-supervised information, we also employ multi-view depth consistency and multi-scale minimum reprojection. These techniques utilize existing structural constraints to yield supervised signals for student model training, without requiring costly ground truth depth information. Our extensive experimental evaluation demonstrates that our proposed method significantly improves the accuracy of the baseline monitored distillation method.

翻译：本文提出了一种新颖的深度补全方法，该方法利用多视图改进的监控蒸馏技术生成更精确的深度图。我们的方法基于当前最先进的集成蒸馏方法，在其中引入立体模型作为教师模型，以提高学生模型在深度补全任务中的准确性。通过在集成蒸馏过程中最小化给定图像的重构误差，我们能够避免学习基于补全的教师模型固有的误差模式。为提供自监督信息，我们还采用了多视图深度一致性和多尺度最小重投影技术。这些技术利用现有的结构约束为学生模型训练生成监督信号，无需昂贵的真实深度标注。大量实验评估表明，我们提出的方法显著提升了基准监控蒸馏方法的准确性。