Monoculaire Estimation de la profondeur (MDE) is a technique in vision par ordinateur that aims to infer the depth information of a scene from a single 2D image. Unlike vision stéréoscopique, which uses two images from different viewpoints to calculate depth, monocular depth estimation repose uniquement sur les indices visuels présents dans une seule image.
This process involves utilizing various AI techniques, particularly deep learning algorithms, to analyze the spatial relationships and features within the image. By training on large datasets containing images with known depth information, neural networks can learn to predict depth maps, which represent the distance of objects from the camera. These depth maps can be crucial for numerous applications, including 3D scene reconstruction, réalité augmentée, and robotics.
Les approches courantes pour la MDE incluent réseaux de neurones convolutifs (CNNs) that process the image data to identify depth patterns based on texture, shading, and object size. The challenge lies in accurately estimating depth without multiple viewpoints, which requires sophisticated models to interpret the complex visual information presented in a single image.
Monocular depth estimation is particularly valuable in scenarios where obtaining stereo images is impractical. It enables advancements in systèmes autonomes, such as self-driving cars and drones, where understanding the environment is critical for navigation and obstacle avoidance.