Depth Estimation Error

Depth estimation error, the inaccuracy in predicting distances from images, is a critical challenge hindering progress in numerous applications like autonomous driving and robotics. Current research focuses on improving depth estimation accuracy through various methods, including novel loss functions, geometric uncertainty modeling, and the development of advanced architectures such as transformers and convolutional neural networks, often incorporating temporal and multi-view information. Reducing depth estimation errors is crucial for enhancing the reliability and safety of these applications, driving ongoing efforts to develop more robust and accurate models.