Laser scanner measurements are corrupted by noise and artifacts that can undermine the performance of registration, segmentation, surface reconstruction, recognition, and other algorithms operating on the data. While much research has addressed laser scanner noise models, comparatively little is known about other artifacts, such as the mixed pixel effect, color-dependent range biases, and specular reflection effects. This paper focuses on the mixed pixel effect and the related challenge of detecting depth discontinuities in 3D data. While a number of algorithms have been proposed for detecting mixed pixels and depth discontinuities, there is no consensus on how well such algorithms perform or which algorithm performs best. This paper presents a comparative analysis of five mixed-pixel/discontinuity detection algorithms on real data sets. We find that an algorithm based on the surface normal angle has the best overall performance, but that no algorithm performs exceptionally well. Factors influencing algorithm performance are also discussed.