3D convolution is a specialized operation used in convolutional neural networks (CNNs) that processes three-dimensional data, such as video frames or volumetric images (e.g., CT scans). Unlike 2D convolution, which operates on two-dimensional input (height and width), 3D convolution adds depth to the operation, capturing temporal information or spatial relations in three dimensions. This technique is particularly valuable in applications like action recognition in videos, where the temporal dynamics and spatial features are crucial for accurate predictions. By applying 3D filters across the dimensions, models can learn richer representations and improve performance in tasks involving 3D data.