Compressive light transport sensing

Figure 1: An example of relit images of a scene generated from a reflectance field captured using just 1000 non-adaptive illumination patterns (emitted from the right onto the scene). The incident lighting resolution, and resolution of each reflectance function, is 128×128. Even though we only performed a small number of measurements, we are still able to capture and represent complex light transport paths. Left: the scene relit with a high frequency illumination condition (inset). Middle: the scene relit under a natural illumination condition (inset). Right: a ground truth reference photograph of the scene. In this paper we propose a new framework for capturing light trans-port data of a real scene, based on the recently developed theory of compressive sensing. Compressive sensing offers a solid math-ematical framework to infer a sparse signal from a limited number of non-adaptive measurements. Besides introducing compressive sensing for fast acquisition of light transport to computer graphics, we develop several innovations that address specific challenges for image-based relighting, and which may have broader implications. We develop a novel hierarchical decoding algorithm that improves reconstruction quality by exploiting inter-pixel coherency relations. Additionally, we design new non-adaptive illumination patterns that minimize measurement noise and further improve reconstruction quality. We illustrate our framework by capturing detailed high-resolution reflectance fields for image-based relighting