Match cost functions are common elements of every stereopsis algorithm that are used to provide a dissimilarity measure between pixels in different images. Global stereopsis algorithms incorporate assumptions about the smoothness of the resulting distance map that can interact with match cost functions in unpredictable ways. In this paper, we present a large-scale study on the relative performance of a structured set of match cost functions within several global stereopsis frameworks. We compare 272 match cost functions that are built from component parts in the context of four global stereopsis frameworks with a data set consisting of 57 stereo image pairs at three different variances of synthetic sensor noise. From our analysis, we infer a set of general rules that can be used to guide derivation of match cost functions for use in global stereopsis algorithms.

Stereo correspondence research often involves the comparison of techniques to determine which are better under different circumstances. The methods of comparison employed often take the form of applying the techniques to a few stereo image pairs with the technique with the lowest error rate declared superior. However, the majority of these comparisons do not contain any discussion of statistical significance; making the declared superiority of a technique statistically unreliable. In this paper we present a new evaluation method called cluster ranking that yields a statistically significant comparison of the stereo techniques being compared. Cluster ranking leverages statistical inference techniques to first rank the performance of stereo techniques on a single stereo image pair and then combine the rankings from multiple stereo pairs into an over-all ranking; in both of these rankings, only stereo techniques that are statistically different are given different ranks. We demonstrate our framework with a comparison of constructable match cost measures (those that can be assembled from a base set of components) on a data set consisting of 30 synthetic stereo pairs, with varying amounts of noise, and 18 scenes from the 2005 and 2006 Middlebury data sets. Our analysis reveals match cost measures, and measure components, that are statistically superior to all other measures depending on amount of noise, illumination, or exposure time.

Adaptive temporal sampling, used to create motion blur in distributed ray tracing, generates more sample points in regions with motion blur than in regions without motion blur. When the number of sample points used on stationary objects in regions with motion blur exceeds the number of sample points used in other regions of the image, the variance in the color of the object can differ between the two regions. This paper identifies the cause of this variance discrepancy, and proposes a modification to existing adaptive temporal sampling algorithms which eliminated it. Our results demonstrate that the variance of stationary objects remains approximately the same throughout the entire image and that the proposed modification is capable of improving the running time of existing adaptive temporal sampling algorithms.