We have witnessed the rapid expansion of systemsthat collect 3D point cloud data in real-time andrely heavily on object-detection models. The precision and classification accuracy of these modelsare crucial in mission-critical applications such as robotics, autonomous vehicles and aviation.However, point cloud data is highly susceptible to various forms of corruption that can severely com-promise model performance. These corruptions may arise from weather distortions, sensor inputerrors, or malicious adversarial attacks targeting the input stream. Robustness against such attacksin 3D has not received the same level of attention as their 2D counterparts.Our work shows that distortions in 3D point cloud data cause a 30% drop in model accuracy. We pro-pose a three step corrective strategy that restores accuracy to over 85% and over 90% in adversarialcases, surpassing both the baseline and state-of- the-art defenses. Importantly, our method main-tains robustness without degrading accuracy in the absence of corruptions or attacks. The defensestrategy and corrective measures proposed in this work are a significant step toward making objectdetection models reliable and resilient in real-time settings, bringing much needed robustness to 3Dclassification models.