A monocular camera-based three-dimensional scene dense reconstruction method, belonging to the field of image processing and computer vision. The method comprises: modeling a camera pose and a three-dimensional scene by using a feature-based simultaneous localization and mapping method, deriving a fusion algorithm of traditional geometric depth estimation and convolutional neural network depth inference to establish a deep computational reconstruction model, and obtaining a high-quality dense depth map using a high-performance solution algorithm. The system is easy to be constructed, has low requirements for hardware resources, and can complete the dense reconstruction of a three-dimensional scene by means of a common monocular camera. The feature-based SLAM method is used to ensure the accuracy of localization, and fuses sparse depth scattering points and a CNN-inferred low-resolution depth map, enabling the recovery result to be more accurate. A fast and efficient algorithm is used to solve a deep reconstruction function, avoiding the problem of large-scale matrix inversion, and the algorithm is high in operation speed, thereby providing a guarantee for monocular camera-based real-time three-dimensional scene dense reconstruction.
MONOCULAR CAMERA-BASED THREE-DIMENSIONAL SCENE DENSE RECONSTRUCTION METHOD