Passive-Active Geometric Calibration for View-Dependent Projections onto Arbitrary Surfaces

In this paper we present a hybrid technique for correcting distortions that appear when projecting images onto geometrically complex, colored and textured surfaces. It analyzes the optical flow that results from perspective distortions during motions of the observer and tries to use this information for computing the correct image warping. If this fails due to an unreliable optical flow, an accurate -but slower and visiblestructured light projection is automatically triggered. Together with an appropriate radiometric compensation, view-dependent content can be projected onto arbitrary everyday surfaces. An implementation mainly on the GPU ensures fast frame rates.

A Physically Based Transmission Model of Rough Surfaces

Transparent and translucent objects involve both light reflection and transmission at surfaces. This paper presents a physically based transmission model of rough surface. The surface is assumed to be locally smooth, and statistical techniques is applied to calculate light transmission through a local illumination area. We have obtained an analytical expression for single scattering. The analytical model has been compared to our Monte Carlo simulations as well as to the previous simulations, and good agreements have been achieved. The presented model has potential applications for realistic rendering of transparent and translucent objects.

An Empirical Study of Non-Rigid Surface Feature Matching of Human from 3D Video

This paper presents an empirical study of affine invariant feature detectors to perform matching on video sequences of people with non-rigid surface deformation. Recent advances in feature detection and wide baseline matching have focused on static scenes. Video frames of human movement capture highly non-rigid deformation such as loose hair, cloth creases, skin stretching and free flowing clothing. This study evaluates the performance of six widely used feature detectors for sparse temporal correspondence on single view and multiple view video sequences. Quantitative evaluation is performed of both the number of features detected and their temporal matching against and without ground truth correspondence. Recall-accuracy analysis of feature matching is reported for temporal correspondence on single view and multiple view sequences of people with variation in clothing and movement. This analysis identifies that existing feature detection and matching algorithms are unreliable for fast movement with common clothing.