Depth imaging is a foundational building block for broad applications, such as autonomous driving and virtual/augmented reality. Traditionally, depth cameras have relied on time-of-flight sensors or multi-lens systems to achieve physical depth measurements. However, these systems often face a trade-off between a bulky form factor and imprecise approximations, limiting their suitability for spatially constrained scenarios. Inspired by the emerging advancements of nano-optics, we present Nano-3D, a metasurface-based neural depth imaging solution with an ultra-compact footprint. Nano-3D integrates our custom-fabricated 700 nm thick TiO2 metasurface with a multi-module deep neural network to extract precise metric depth information from monocular metasurface-polarized imagery. We demonstrate the effectiveness of Nano-3D with both simulated and physical experiments. We hope the exhibited success paves the way for the community to bridge future graphics systems with emerging nanomaterial technologies through novel computational approaches.

翻译：深度成像是自动驾驶、虚拟/增强现实等广泛应用的基础构建模块。传统深度相机通常依赖飞行时间传感器或多镜头系统来实现物理深度测量。然而，这些系统往往在笨重的物理形态与不精确的近似估算之间面临权衡，限制了其在空间受限场景中的适用性。受纳米光学新兴进展的启发，我们提出了Nano-3D——一种基于超表面的神经深度成像解决方案，具有超紧凑的物理尺寸。Nano-3D将我们定制加工的700纳米厚二氧化钛超表面与一个多模块深度神经网络相结合，从单目超表面偏振图像中提取精确的度量深度信息。我们通过仿真实验与物理实验验证了Nano-3D的有效性。我们希望所展示的成功能够为学界通过新颖的计算方法，将未来图形系统与新兴纳米材料技术相融合开辟道路。