This letter addresses a depth-completion problem for sequential data to reconstruct 3D models of outdoor scenes. While many deep-learning-based approaches have recently achieved promising results, their results are not directly applicable to 3D modeling because of several reasons. First, most results contain a lot of outliers because of irregularly distributed sparse measurements. Second, they ignore temporal coherence in sequential frames and produce temporally inconsistent depths. Therefore, we propose a new method that predicts temporally consistent depths with corresponding confidences from sequential frames. The suggested method can efficiently remove the outliers based on confidence estimates, which accurately represent the true prediction errors. The method also produces temporally consistent depths by integrating the depth information of consecutive frames. In addition, we present a 3D-modeling system that reconstructs a globally consistent 3D model in real-time using the results from the proposed depth completion method. Extensive experiments on synthetic and real-world datasets show that our method outperforms the other state-of-the-art methods in terms of both depth-completion and 3D-modeling accuracies.

中文翻译：

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用于 3D 模型重建的具有置信度估计的顺序深度完成

这封信解决了序列数据的深度补全问题，以重建室外场景的 3D 模型。虽然许多基于深度学习的方法最近取得了可喜的结果，但由于多种原因，它们的结果并不直接适用于 3D 建模。首先，由于不规则分布的稀疏测量，大多数结果都包含大量异常值。其次，它们忽略了连续帧中的时间相干性并产生时间上不一致的深度。因此，我们提出了一种新方法，可以从连续帧中预测具有相应置信度的时间一致深度。建议的方法可以根据置信估计有效地去除异常值，准确表示真实的预测误差。该方法还通过整合连续帧的深度信息来产生时间上一致的深度。此外，我们提出了一个 3D 建模系统，该系统使用所提出的深度补全方法的结果实时重建全局一致的 3D 模型。对合成和真实世界数据集的大量实验表明，我们的方法在深度完成和 3D 建模精度方面都优于其他最先进的方法。