DELTAR:基于轻量级ToF传感器和RGB图像的深度估计

Yijin Li, Xinyang Liu, Wenqian Dong, Han Zhou, H. Bao, Guofeng Zhang, Yinda Zhang, Zhaopeng Cui
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引用次数: 8

摘要

轻型飞行时间(ToF)深度传感器体积小、价格便宜、能耗低,已被大量应用于移动设备上,用于自动对焦、障碍物检测等。然而,由于测量的是特定区域的深度分布,而不是某像素的深度值,而且分辨率极低,对于3D重建等需要高保真深度的应用来说是不够的。在本文中,我们提出了一种新颖的DELTAR方法,通过与彩色图像的配合,使轻型ToF传感器具有高分辨率和精确深度的测量能力。为了有效地融合颜色域和ToF域的信息,提出了基于深度分布的特征提取器和基于注意力的神经结构,作为DELTAR的核心。为了在实际场景中评估我们的系统,我们设计了一个数据收集设备,并提出了一种校准RGB相机和ToF传感器的新方法。实验表明,与现有的深度完井和深度超分辨率框架相比,我们的方法可以产生更精确的深度,并达到与商品级RGB-D传感器相当的性能。代码和数据可在https://zju3dv.github.io/deltar/上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DELTAR: Depth Estimation from a Light-weight ToF Sensor and RGB Image
Light-weight time-of-flight (ToF) depth sensors are small, cheap, low-energy and have been massively deployed on mobile devices for the purposes like autofocus, obstacle detection, etc. However, due to their specific measurements (depth distribution in a region instead of the depth value at a certain pixel) and extremely low resolution, they are insufficient for applications requiring high-fidelity depth such as 3D reconstruction. In this paper, we propose DELTAR, a novel method to empower light-weight ToF sensors with the capability of measuring high resolution and accurate depth by cooperating with a color image. As the core of DELTAR, a feature extractor customized for depth distribution and an attention-based neural architecture is proposed to fuse the information from the color and ToF domain efficiently. To evaluate our system in real-world scenarios, we design a data collection device and propose a new approach to calibrate the RGB camera and ToF sensor. Experiments show that our method produces more accurate depth than existing frameworks designed for depth completion and depth super-resolution and achieves on par performance with a commodity-level RGB-D sensor. Code and data are available at https://zju3dv.github.io/deltar/.
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