We present a method for reconstructing 3D shape of arbitrary Lambertian objects based on measurements by miniature, energy-efficient, low-cost single-photon cameras. These cameras, operating as time resolved image sensors, illuminate the scene with a very fast pulse of diffuse light and record the shape of that pulse as it returns back from the scene at a high temporal resolution. We propose to model this image formation process, account for its non-idealities, and adapt neural rendering to reconstruct 3D geometry from a set of spatially distributed sensors with known poses. We show that our approach can successfully recover complex 3D shapes from simulated data. We further demonstrate 3D object reconstruction from real-world captures, utilizing measurements from a commodity proximity sensor. Our work draws a connection between image-based modeling and active range scanning and is a step towards 3D vision with single-photon cameras.

翻译：我们提出了一种基于微型、节能、低成本单光子相机测量结果的重建任意朗伯体三维形状的方法。这些相机作为时间分辨图像传感器，利用超快漫射光脉冲照射场景，并以高时间分辨率记录从场景返回的脉冲波形。我们对该成像过程进行建模，考虑其非理想特性，并调整神经渲染方法以从一组具有已知位姿的空间分布传感器中重建三维几何结构。实验表明，我们的方法能够从仿真数据中成功恢复复杂的三维形状。此外，我们利用商用接近传感器的实际测量数据，进一步展示了真实场景下的三维物体重建效果。本研究建立了基于图像的建模与主动测距扫描之间的关联，是迈向单光子相机三维视觉的重要一步。