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.

基于单光子相机的能效高、低成本小型测量，我们提出了一种重建任意兰伯特物体 3D 形状的方法。通过模拟数据和实际捕捉的测量结果来展示我们的方法可以成功恢复复杂的 3D 形状，连接了基于图像建模和主动式范围扫描，并为单光子相机的 3D 视觉迈出了一步。