  The problem of searching for a model-based scene interpretation is analyzed
within a probabilistic framework. Object models are formulated as generative
models for range data of the scene. A new statistical criterion, the truncated
object probability, is introduced to infer an optimal sequence of object
hypotheses to be evaluated for their match to the data. The truncated
probability is partly determined by prior knowledge of the objects and partly
learned from data. Some experiments on sequence quality and object segmentation
and recognition from stereo data are presented. The article recovers classic
concepts from object recognition (grouping, geometric hashing, alignment) from
the probabilistic perspective and adds insight into the optimal ordering of
object hypotheses for evaluation. Moreover, it introduces point-relation
densities, a key component of the truncated probability, as statistical models
of local surface shape.
