Gaze is an excellent metric for understanding human attention. However, research on identifying gaze behaviors saccades, fixations, and smooth pursuits for example for large (i e more than one meter viewing distance), interactive 3D scenes with virtual reality headsets is still in its early stages. The understanding of gaze behaviors is affected by equipment, user, the types of scenarios targeted, etc. There is currently little to no consensus on how to select gaze behavior identification methods, and what the impact of this choice has on the validation of research hypotheses on human attention This work investigates the impact of gaze behavior identification approaches on human gaze data analysis by re-implementing six state of the art identification algorithms for VR To underline the potential of the system, we design a 3D scene with various animated and interactive 2D and 3D stimuli with which we collected eye tracking data for 20 participants through a study. We then provide disaggregated analyses on metrics from the literature for various methods and stimuli. From what we observe with a current state of analysis, participants are tend to have longer fixations on dynamic 3D stimulus than on static ones, while some algorithms (based on their nature) fail to detect fixations on specific stimuli for some of the participants. We should also underline the differences in fixation patterns for participants as their fixations differ in quantity and duration across the algorithms.