This paper presents a comprehensive review and comparative evaluation of feature extraction techniques for 3D point cloud data, which play a pivotal role in autonomous systems, robotics, and 3D scene understanding. Classical methods such as FPFH and SHOT offer low-latency, resource-efficient processing but struggle with geometric complexity and noise sensitivity. In contrast, deep learning-based architectures like KPConv, RandLA-Net, and PAConv exhibit enhanced accuracy and robustness at the cost of increased computational overhead. Comparative analysis is conducted using metrics including classification accuracy, inference time, noise resilience, and computational complexity. Recent models such as ECGNet and VTNet achieve state-of-the-art performance, making them suitable for high-fidelity applications, whereas traditional descriptors remain effective in real-time, resource-limited scenarios. The findings underscore the importance of selecting methods based on operational constraints, advocating a trade-off-driven approach to point cloud feature extraction.