Self-supervised monocular depth prediction is a widely researched field that aims to provide a better scene understanding. However, most existing methods prioritize prediction accuracy over computation cost, which can hinder the deployment of these methods in real-world applications. Our objective is to propose a solution that efficiently compresses the depth map while maintaining a high level of accuracy for navigation purpose. The proposed method is an expansion of the work presented in N-QGN, which utilizes a quadtree representation for compression. This approach has already shown promising results, but we aim to improve it further by making it more accurate, faster, and easier to train. Therefore, we introduce a new method that directly predicts the quadtree structure, resulting in a more consistent prediction, and we revise the network architecture to be lighter and produce state-of-the-art accuracy results, depending on the data compression rate. The new implementation is also faster, making it more suitable for real-time applications. Experiments have been conducted on various scene configuration highlighting the capability of the method to efficiently predicting a reliable quadtree depth representation of the scene at low computation cost and high accuracy.

中文翻译：

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N-QGNv2：预测单目相机深度图的最佳四叉树表示

自监督单目深度预测是一个广泛研究的领域，旨在提供更好的场景理解。然而，大多数现有方法优先考虑预测准确性而不是计算成本，这可能会阻碍这些方法在实际应用中的部署。我们的目标是提出一种解决方案，可以有效压缩深度图，同时保持导航的高精度。所提出的方法是 N-QGN 中提出的工作的扩展，它利用四叉树表示进行压缩。这种方法已经显示出有希望的结果，但我们的目标是通过使其更准确、更快、更容易训练来进一步改进它。因此，我们引入了一种直接预测四叉树结构的新方法，从而产生更一致的预测，并且我们将网络架构修改为更轻，并根据数据压缩率产生最先进的精度结果。新的实现速度也更快，使其更适合实时应用程序。已经对各种场景配置进行了实验，突出了该方法以低计算成本和高精度有效预测场景的可靠四叉树深度表示的能力。