Stereo Matching

Stereo matching aims to reconstruct 3D depth from two or more images by identifying corresponding pixels across different viewpoints. Current research heavily utilizes deep learning, focusing on improving accuracy and efficiency through novel architectures like Transformers and convolutional neural networks, often incorporating iterative refinement and uncertainty estimation techniques. This field is crucial for applications such as autonomous driving, robotics, and 3D modeling, with ongoing efforts to enhance generalization across diverse datasets and improve real-time performance on resource-constrained devices.