Object recognition in Point Cloud data from LiDAR sensors often faces challenges like noise, clutter, and ground interference, significantly affecting tasks such as segmentation, classification, and detection. To address these issues, we introduced a framework comprising a denoiser and a classifier, enhancing the robustness of LiDAR-based object recognition. The denoiser plays a crucial role in noise mitigation and operates as a two-part system, utilizing ScoreNet and the Guided Filter. ScoreNet employs advanced scoring techniques to separate valuable information from noise, while the Guided Filter further refines the data, preserving crucial details. The output from the denoiser seamlessly feeds into the classifier, leveraging a modified GDANet architecture with depthwise overparameterized convolution (DOConv) to capture intricate features. We evaluated our approach using Point-to-Point, Hausdorff distance, and Accuracy metrics, comparing it with other denoising methods and point cloud classifiers. Our models demonstrated significant improvements in denoising and classification tasks, with the denoiser achieving outstanding results in the Hausdorff Distance metric, reaching a score of 0.177. Simultaneously, the classifier outperformed other point cloud classifiers, achieving accuracy scores of 90.7% and 96.7% for ModelNet40-C and Human Pose Dataset, respectively. These achievements underscore the importance of our framework in addressing the challenges of noise and clutter in Point Cloud data, ultimately advancing LiDAR-based object recognition.