The Hough Transform is a well-known method for detecting parameterized objects. It is the de facto standard for detecting lines and circles in 2-dimensional data sets. For 3D it has attained little attention so far. Even for the 2D case high computational costs have lead to the development of numerous variations for the Hough Transform. In this article we evaluate different variants of the Hough Transform with respect to their applicability to detect planes in 3D point clouds reliably. Apart from computational costs, the main problem is the representation of the accumulator. Usual implementations favor geometrical objects with certain parameters due to uneven sampling of the parameter space. We present a novel approach to design the accumulator focusing on achieving the same size for each cell and compare it to existing designs.