  Scene understanding under low-light conditions is a challenging problem. This
is due to the small number of photons captured by the camera and the resulting
low signal-to-noise ratio (SNR). Single-photon cameras (SPCs) are an emerging
sensing modality that are capable of capturing images with high sensitivity.
Despite having minimal read-noise, images captured by SPCs in photon-starved
conditions still suffer from strong shot noise, preventing reliable scene
inference. We propose photon scale-space a collection of high-SNR images
spanning a wide range of photons-per-pixel (PPP) levels (but same scene
content) as guides to train inference model on low photon flux images. We
develop training techniques that push images with different illumination levels
closer to each other in feature representation space. The key idea is that
having a spectrum of different brightness levels during training enables
effective guidance, and increases robustness to shot noise even in extreme
noise cases. Based on the proposed approach, we demonstrate, via simulations
and real experiments with a SPAD camera, high-performance on various inference
tasks such as image classification and monocular depth estimation under ultra
low-light, down to < 1 PPP.
