Depth Prediction

Depth prediction, the task of estimating distances from images or sensor data, aims to provide machines with a 3D understanding of their environment. Current research heavily focuses on improving the accuracy and robustness of monocular depth estimation, employing various deep learning architectures like convolutional neural networks and transformers, often incorporating additional sensor data (e.g., radar, LiDAR) or geometric constraints to overcome challenges like scale ambiguity and low-texture regions. These advancements are crucial for applications in robotics, autonomous driving, and 3D scene reconstruction, enabling more reliable and efficient navigation, object recognition, and scene understanding. The field is also actively exploring self-supervised and semi-supervised learning techniques to reduce reliance on large, manually labeled datasets.