Coaxial LightMicroscope

Learning Geometric-aware Properties in 2D Representation Using Lightweight CAD Models, or Zero Real 3D Pairs

Coaxial lightled

Coaxial lightsource

Our technique can estimate lighting for a variety of input images, such as indoor and outdoor scenes, close-up shots, paintings, and photos of human faces. Here, we show our predicted chrome balls in a normally exposed version (EV0) and an underexpoed version (EV-5). These input images are from Unsplash.com.

Our method can generate multiple plausible chrome balls by varying the initial noise map of diffusion sampling. Note that the average of these variations captures the overall lighting reasonably well. This average is utilized by our "iterative inpainting" algorithm to enhance the quality and consistency of light estimation. Hover over these videos to pause.