In this paper we present a hierarchical Monte-Carlo radiosity algorithm driven by the view importance. The algorithm makes to possible to concentrate the computational effort on solution in the immediate environment of the observer, trading the low solution quality in invisible areas for better quality in areas that are visible for the observer. This is achieved by modifying the sampling probabilities of scene elements so that more samples are concentrated in the area of high importance and by extending the subdivision oracle function so that the subdivision is coarser in areas of low importance. This paper extends the previous work by introducing a combination of hierarchical refinement and view importance driven method for Monte-Carlo radiosity.