Omnidirectional Depth

Omnidirectional depth estimation aims to reconstruct three-dimensional scenes from 360° images, crucial for applications like robotics and autonomous driving. Current research focuses on robust and efficient algorithms, often employing multi-view stereo matching techniques with various camera configurations (e.g., multiple fisheye cameras, cylindrical panoramas) and incorporating neural network architectures like CNNs and Transformers to handle image distortion and achieve real-time performance. These advancements improve accuracy and generalization, particularly in challenging real-world conditions, leading to more reliable 3D scene understanding for a range of applications.