Viewpoint Rendering Fusion for Artifact-Resistant 3D Images

The patent replaces traditional mechanical 3D generation methods (requiring multiple cameras, manual calibration, and complex post-processing) with a deep learning-based neural network system. The neural network automatically learns depth information and viewpoint transformations from 2D images, eliminating the need for expensive multi-camera setups and manual 3D reconstruction processes, thereby reducing both time and cost while improving quality.

The patent uses a neural network to learn and copy the transformation patterns from training data consisting of 2D images and their corresponding 3D viewpoint images. Once trained, the network can rapidly generate new viewpoint images by applying learned transformation rules to input 2D images, avoiding the need for time-consuming traditional 3D reconstruction and rendering processes.

The patent performs preliminary action by training the neural network offline on large datasets of 2D images with known depth information or multiple viewpoint images. During this pre-training phase, the network learns to infer depth and spatial relationships from 2D images alone. Once trained, the network can generate accurate viewpoint images without requiring actual depth images or complex multi-camera systems during operation, thereby simplifying the deployed system while maintaining quality.

The patent introduces a neural network as an intermediary that bridges the gap between simple 2D images and complex 3D viewpoint generation. Instead of directly using complex depth sensing hardware or multi-camera systems, the neural network acts as a computational intermediary that infers depth information and generates viewpoint transformations, simplifying the overall system architecture while maintaining output quality.

The patent transforms the image distortion problem from a geometric parameter adjustment task to a learned parameter optimization task. Instead of using fixed geometric distortion models that require manual tuning and are sensitive to parameter errors, the neural network learns optimal distortion parameters and transformation patterns from training data. This allows the system to adapt to different image contents and conditions, improving robustness while maintaining ease of operation.

The patent incorporates feedback mechanisms during the neural network training process, where the network's generated viewpoint images are compared against ground truth images from multiple cameras or depth sensors. The loss function provides feedback signals that guide the network to minimize artifacts and improve transformation accuracy. This feedback-driven learning approach enables the system to achieve high robustness without requiring manual tuning of distortion parameters.