In the field of image acquisition, Dynamic Vision Sensors (DVS) present an innovative methodology, capturing only the variations in pixel brightness instead of absolute values and thereby revealing unique features. Given that the primary deployment of DVS is within embedded systems characterized by their constrained transmission and storage capabilities, the emphasis on data compression becomes significant. Nonetheless, such a compression could potentially compromise the efficacy of computer vision (CV) applications. This study investigates the implications of a lossy compression technique, premised on point cloud representation, for event data in CV tasks. Multiple scenarios under various compression intensities are applied to event data, and the experiments indicate the feasibility of attaining reduced bitrates while incurring minimal impact in CV task performance.