论文标题
使用深度注意体积的指导单眼估计
Guiding Monocular Depth Estimation Using Depth-Attention Volume
论文作者
论文摘要
从单个图像中恢复场景的深度是一个不适的问题，它需要额外的先验，通常称为单眼深度提示，以消除不同的3D解释。在最近的著作中，这些先验是通过使用深层神经网络以端到端方式从大型数据集中学习的。在本文中，我们提出指导深度估计，以偏爱普遍存在的平面结构，尤其是在室内环境中。这是通过将非本地的共同性约束纳入网络中的，具有一种称为Depth Investention量（DAV）的新型关注机制。在两个流行的室内数据集（即NYU-DEPTH-V2和SCANNET）上进行的实验表明，我们的方法可以实现最新的深度估计结果，而仅使用竞争方法所需的参数数量的一小部分。
Recovering the scene depth from a single image is an ill-posed problem that requires additional priors, often referred to as monocular depth cues, to disambiguate different 3D interpretations. In recent works, those priors have been learned in an end-to-end manner from large datasets by using deep neural networks. In this paper, we propose guiding depth estimation to favor planar structures that are ubiquitous especially in indoor environments. This is achieved by incorporating a non-local coplanarity constraint to the network with a novel attention mechanism called depth-attention volume (DAV). Experiments on two popular indoor datasets, namely NYU-Depth-v2 and ScanNet, show that our method achieves state-of-the-art depth estimation results while using only a fraction of the number of parameters needed by the competing methods.
