The demand for high-quality 3D visualizations has surged across various professional fields, prompting significant advancements in computer graphics. One such advancement is 3D Gaussian Splatting, a technique evolving from Lee Westover’s splatting concept introduced in 1990. This study investigates the potential of 3D Gaussian Splatting to enhance PointNet classification techniques for 3D objects. Utilizing the Princeton ModelNet10 dataset, I convert 3D models into point clouds, applying 3D Gaussian Splatting to generate optimized Gaussian representations. These representations are used to train PointNet++ models with various feature configurations, including position, scale, rotation, opacity, and spherical harmonics. Our findings reveal that while 3D Gaussian Splatting enables effective 3D object classification, it does not outperform traditional methods that utilize ground truth data directly sampled from object surfaces. Nonetheless, the method demonstrates comparable accuracy, suggesting its viability in scenarios where initial meshes are unavailable. This research highlights the potential and limitations of 3D Gaussian Splatting in advancing 3D object classification.