Monocular 3D Detection

Monocular 3D object detection aims to accurately locate and classify objects in three-dimensional space using only a single camera image, a challenging task due to the inherent lack of depth information. Current research focuses on improving accuracy and efficiency through techniques like incorporating geometric priors, leveraging depth estimation networks (often trained with auxiliary data such as LiDAR or stereo images), and employing advanced architectures such as transformers and convolutional neural networks, sometimes with innovative loss functions or data augmentation strategies. These advancements are crucial for applications like autonomous driving and robotics, where reliable 3D perception is essential for safe and efficient operation. The field is actively exploring weakly-supervised and semi-supervised learning methods to reduce reliance on expensive and time-consuming 3D annotation.