Cost Volume

Cost volume methods are central to various computer vision tasks, aiming to efficiently represent and aggregate matching information between image pairs or across multiple views for depth estimation, optical flow calculation, and stereo matching. Current research focuses on improving the efficiency and accuracy of cost volume construction and aggregation, often employing novel architectures like transformers and lightweight convolutional networks (e.g., GhostNet) to reduce computational complexity and memory usage while enhancing performance. These advancements are significantly impacting applications such as autonomous driving, 3D scene reconstruction, and augmented reality by enabling more robust and real-time processing of high-resolution imagery.