A multi-class sampling component (MCSC) is described for selecting samples associated with two or more sampling classes to produce output information. The overall set of samples in the output information exhibits a desirable Poisson distribution. Further, each subset of samples associated with each respective class exhibits a Poisson distribution. The MCSC selects samples based on intra-class radius information (describing the minimum allowed distances between same-class samples) and inter-class radius information (describing the minimum allowed distances between different-class samples). The MCSC can be applied to different applications, such as an object placement application, a color stippling application, a sensor design application, and so on.