Multi-Image Alignment Training With Synthetic Shifted Copies

The patent creates artificial misaligned copies of reference images by applying shift operations to simulate misalignment. These synthetic copies serve as training data for the neural network, eliminating the need for complex infrared depth sensors or manual refinement processes. The copying principle allows generating unlimited training samples from a single reference image through various shift transformations.

The system uses the reference images themselves to generate the misaligned training data needed for training. Instead of requiring external depth sensors or manual annotation, the reference images are shifted and processed to create the training dataset, making the system self-sufficient and avoiding additional complex hardware or manual intervention.

The patent performs misalignment simulation and training data preparation in advance during the training phase. By pre-training the neural network on artificially misaligned images, the system learns to correct misalignments automatically during inference, eliminating the need for time-consuming post-production refinement processes when actual synthesis is performed.

The patent replaces manual post-production refinement processes with an automated neural network-based alignment system. The neural network learns alignment corrections from training data and automatically applies them during synthesis, substituting manual mechanical adjustment processes with automated computational methods that are faster and more consistent.

The patent generates synthetic training samples by creating shifted copies of reference images. Instead of requiring manual annotation of real misaligned image pairs, the system copies reference images and applies deterministic shift operations to create artificially misaligned versions. This copying approach generates unlimited annotated training data automatically without any manual annotation effort.

The training data generation process is self-service, where the system automatically creates and annotates training samples without external human intervention. The reference images serve as both the source material and the ground truth, and the shift operations automatically provide the annotation information (shift vectors) needed for training the neural network.

The patent applies different shift operations to different regions or aspects of the image processing pipeline. By varying the shift vectors and applying them in different training samples, the system creates diverse training scenarios that cover various misalignment conditions. This local variation in shift application maintains training efficiency while improving the realism and coverage of training scenarios.

The patent varies the parameters of the shift operations (shift magnitude, direction, and combination with other transformations) to create diverse training samples. By changing these parameters systematically, the training data becomes more realistic and covers a broader range of possible misalignment scenarios, while still maintaining the computational efficiency of simple shift operations.