Light Structure from Pin Motion: Geometric Point Light Source Calibration

Abstract

We present a method for geometric point light source calibration. Unlike prior works that use Lambertian spheres, mirror spheres, or mirror planes, we use a calibration target consisting of a plane and small shadow casters at unknown positions above the plane. We show that shadow observations from a moving calibration target under a fixed light follow the principles of pinhole camera geometry and epipolar geometry, allowing joint recovery of the light position and 3D shadow caster positions, equivalent to how conventional structure from motion jointly recovers camera parameters and 3D feature positions from observed 2D features. Moreover, we devised a unified light model that works with nearby point lights as well as distant light in one common framework. Our evaluation shows that our method yields light estimates that are stable and more accurate than existing techniques while having a much simpler setup and requiring less manual labor.

Learning Rotation-Invariant Representations of Point Clouds Using Aligned Edge Convolutional Neural Networks

“…Existing methods use various kinds of customized calibration targets: [19] [20] propose to use a target of AprilTags and pins to infer the position of point light source from the shadows of the pins. Alternatively, [21] [22] propose to use a Lambertian sphere and estimate the light source parameters by learning to reconstruct the sphere.…”