Panoramic Depth

Panoramic depth estimation aims to reconstruct three-dimensional scene geometry from a single 360-degree image, overcoming the limitations of traditional narrow-field-of-view cameras. Current research focuses on developing robust and efficient algorithms, employing architectures like convolutional neural networks and transformers, often incorporating techniques like stereo matching, multi-projection fusion, and depth completion networks to handle distortions and missing data. This field is crucial for advancing applications such as augmented and virtual reality, robotics, autonomous driving, and lighting simulation, where comprehensive scene understanding is essential. Improved accuracy and efficiency in panoramic depth estimation directly translates to better performance in these diverse applications.