Depth Ambiguity

Depth ambiguity, the inherent difficulty in recovering three-dimensional information from two-dimensional images, is a central challenge in computer vision. Current research focuses on mitigating this ambiguity through improved model architectures, such as diffusion models and those incorporating differentiable global positioning, often leveraging multi-view data or incorporating contextual information to constrain depth estimations. These advancements aim to improve the accuracy of tasks like 3D pose estimation, object detection, and novel view synthesis, impacting fields ranging from robotics and augmented reality to medical imaging and autonomous driving. The development of more robust and accurate depth estimation techniques is crucial for bridging the gap between 2D and 3D understanding in computer vision.