Wide Baseline

Wide-baseline techniques aim to accurately estimate depth and reconstruct 3D scenes from images taken with significant camera separation, overcoming challenges like occlusion and appearance changes between views. Current research focuses on developing robust algorithms and neural network architectures, such as graph neural networks and transformers, to handle the increased complexity of wide-baseline data, often incorporating techniques like curriculum learning and differentiable solvers for pose estimation. These advancements are crucial for applications ranging from autonomous navigation and immersive virtual reality to underwater environmental monitoring, enabling more accurate 3D scene understanding from sparse or challenging visual data.