4 Dimensional Convolution

Four-dimensional (4D) convolutions extend traditional convolutional neural networks to process spatiotemporal data, aiming to improve the analysis of dynamic scenes and volumetric data. Current research focuses on applying 4D convolutions within various architectures, including voxel networks and sparse convolution methods, often coupled with techniques like spatio-temporal decomposition to mitigate computational costs. This approach shows promise in diverse applications such as autonomous driving (scene flow estimation, moving object segmentation), 3D mesh generation, and light field image processing, offering improvements in accuracy and robustness over 2D or 3D-only methods.