No abstract

We present a technique which allows capture of 3D surface geometry and a useful class of BRDFs using extremely simple equipment. A standard digital camera with an attached flash serves as a portable capture device, which may be used to sample geometry to very high resolution, as well as supplying samples over a large portion of the 4D space on which the BRDF is defined. Importantly, it allows capture of extended samples which may have spatially varying (inhomogeneous) BRDF. We demonstrate the system by capturing the geometry of complex materials with varying albedo and BRDF. We show in-situ capture of materials such as a brick wall and a human hand. The limitations of the system are that samples should be roughly planar, and that the BRDF should have some diffuse component in order that a first approximation to the normals can be computed. However, given the simplicity and ease of use of the system (it takes a few minutes to carefully capture a hand), and the ability to capture extended surfaces without any range capture device such as a laser scanner we argue that it is a valuable addition to the range of real-world BRDF capture systems in the literature. We extend standard photometric stereo techniques by moving both the camera and the light source. By incorporating automatic parallax correction we allow the capture of surfaces which are quite far from planar.

Reliable detection of fiducial targets in real-world images is addressed in this paper. We show that even the best existing schemes are fragile when exposed to other than laboratory imaging conditions, and introduce an approach which delivers significant improvements in reliability at moderate computational cost. The key to these improvements is in the use of machine learning techniques, which have recently shown impressive results for the general object detection problem, for example in face detection. Although fiducial detection is an apparently simple special case, this paper shows why robustness to lighting, scale and foreshortening can be addressed within the machine learning framework with greater reliability than previous, more ad-hoc, fiducial detection schemes.

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The recently introduced Rational Function (RF) model permits a linear solution of epipolar geometry and lens distortion from image correspondences for a very general class of lenses. In this paper we show that the model also permits a very elegant form of the plumbline constraint which allows uncalibrated correction of lens distortion from a single image containing lines known to be straight in the world. We show how this may be expressed as a factorization problem, and discuss its behaviour with noisy data. We also introduce a simple reduced parametrization of the RF model which again models a range of existing lenses. Because the RF model provides a very simple form for the distorted lines, nonlinear minimization can compute the Sampson distance to the distorted lines, allowing fast and accurate estimation of the RF model even from noisy images.