A general strategy is to perform the 2 by 3 repeated measures ANOVA. If you get an interaction effect, then you can go on to perform an analysis of simple effects.
An analysis of simple effects involves examining an effect of one factor (e.g., condition) at each level of another factor (e.g., intensity). Thus, if you have an interaction you would be performing both repeated measures ANOVA and the set of paired-samples t-tests. If you don't get an interaction, then the main effect of the focal factor (e.g., condition) would provide a parsimonious hypothesis test.
There are several benefits to this approach. If there are no interaction effects, then main effects provide a parsimonious description of the data. If you are not especially interested in the main effect of intensity,then you don't have to spend much time discussing it. If there is an interaction effect, then you can go on to explore that using analysis of simple effects.
Note also, that another approach is to explicitly model the nature of the interaction. This is particularly interesting where you have a few more levels to intensity. For example, you might posit that there is a linear effect of intensity, and that there is an interaction effect between condition and a linear effect of intensity. Or you could examine an interaction between condition and some non-linear effect of intensity. However, with only three levels of condition, there isn't a lot of information for making such distinctions.