Bruce Lamond

Compressive light transport sensing

In this article we propose a new framework for capturing light transport data of a real scene, based on the recently developed theory of compressive sensing. Compressive sensing offers a solid mathematical framework to infer a sparse signal from a limited number of nonadaptive 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 interpixel coherency relations. Additionally, we design new nonadaptive 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.

Relighting human locomotion with flowed reflectance fields

We present an image-based approach for capturing the appearance of a walking or running person so they can be rendered realistically under variable viewpoint and illumination. In our approach, a person walks on a treadmill at a regular rate as a turntable slowly rotates the persons direction. As this happens, the person is filmed with a vertical array of high-speed cameras under a time-multiplexed lighting basis, acquiring a seven-dimensional dataset of the person under variable time, illumination, and viewing direction in approximately forty seconds. We process this data into a flowed reflectance field using an optical flow algorithm to correspond pixels in neighboring camera views and time samples to each other, and we use image compression to reduce the size of this data. We then use image-based relighting and a hardware-accelerated combination of view morphing and light field rendering to render the subject under user-specified viewpoint and lighting conditions. To composite the person into a scene, we use an alpha channel derived from back lighting and a retroreflective treadmill surface and a visual hull process to render the shadows the person would cast onto the ground. We demonstrate realistic composites of several subjects into real and virtual environments using our technique.

Image-based separation of diffuse and specular reflections using environmental structured illumination

We present an image-based method for separating diffuse and specular reflections using environmental structured illumination. Two types of structured illumination are discussed: phase-shifted sine wave patterns, and phase-shifted binary stripe patterns. In both cases the low-pass filtering nature of diffuse reflections is utilized to separate the reflection components. We illustrate our method on a wide range of example scenes and applications.

Fast image-based separation of diffuse and specular reflections

We present a novel image-based method for separating diffuse and specular reflections of real objects under distant environmental illumination. By illuminating a scene with only four high frequency illumination patterns, the specular and diffuse reflections can be separated by computing the maximum and minimum observed pixel values. Furthermore, we show that our method can be extended to separate diffuse and specular components under image-based environmental illumination. Applications range from image-based modeling of reflectance properties to improved normal and geometry acquisition.

Free-form polarized spherical illumination reflectometry

We present a prototype system for in-situ measurement of per-pixel appearance parameters (i.e., surface orientation, diffuse albedo, specular albedo, and specular roughness) of general scenes. The proposed system requires no specialized hardware, is light weight, and requires no on-site calibration. This makes our system particularly well suited for capturing the appearance of real-world scenes under uncontrolled conditions.