In this paper, we describe the components of a robust algorithm for the detection of vanishing points in man-made environments. We designed our approach to work under quite general conditions (e.g., uncalibrated camera); and in contrast to several other approaches, the assumption of a dominant line-alignment w.r.t. the orthogonal axes of the world coordinate frame (Manhattan world) is not explicitly exploited. Our only premise is, that if a significant number of the imaged line segments meet very accurately in a point, this point is very likely to be a good candidate for a real vanishing point. For finding such points under a wide range of conditions, we propose a flexible algorithmic pipeline that combines accurate line detection techniques with robust statistical candidate initialization and refinement stages.

The method was evaluated on a set of images exhibiting largely varying characteristics concerning image quality and scene complexity. Experiments show that the method, despite the variations, works in a stable manner and that its performance compares favorably with the state of the art.