Depth Inference

Depth inference, the process of estimating distances from images, is crucial for applications like autonomous driving and robotics. Current research focuses on improving accuracy and efficiency using various approaches, including convolutional neural networks (CNNs), transformers, and hybrid architectures that leverage both. These models often incorporate techniques like self-supervised learning, uncertainty modeling, and the use of scene priors (e.g., ground plane) to overcome the inherent ambiguity of monocular depth estimation. Advances in this field directly impact the development of robust and reliable perception systems for a wide range of applications.