3D Model Alignment Using Neural Network Pose Estimation

The patent replaces traditional mechanical object recognition methods with a neural network-based deep learning system. The neural network automatically learns feature representations from images and performs pose estimation, substituting manual feature extraction and matching algorithms with an intelligent system that achieves higher alignment accuracy between 3D models and 2D images.

The patent transitions from 2D image processing to 3D pose estimation by introducing depth information. The neural network estimates three-dimensional pose parameters (position and orientation) from two-dimensional images, adding a spatial dimension to the recognition process and enabling accurate alignment of 3D models with their corresponding 2D projections.

The patent performs preliminary action by pre-training the neural network with large amounts of training data before deployment. The network learns robust feature representations and pose estimation capabilities in advance, enabling it to achieve high alignment accuracy with minimal input images during actual operation. This preliminary training phase stores knowledge that eliminates the need for multiple input images during runtime.

The neural network performs self-service by automatically learning and adapting to different objects and scenarios during training. Once trained, the system can independently perform accurate pose estimation and alignment without requiring multiple input images or complex preprocessing, making the apparatus more available and easier to operate in various real-world conditions.