3D Estimator Neural Network Training via Multi-View Rendering
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Solution Overview
Problem
Training 3D estimator neural networks to accurately generate 3D shapes and textures from images is challenging due to limited training pairs and the issue of network fooling, where the network generates outputs that visually differ from the original image despite matching image features.
Innovation Solution
The system employs an unsupervised training loop using a single image to train the 3D estimator neural network, generating multiple views of the estimated 3D shape and texture to mitigate network fooling, and optionally uses a supervised training loop with synthetic images to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a 3D estimator neural network is trained using traditional supervised learning with limited training pairs, then the training process is simpler, but the accuracy of 3D shape and texture estimation is poor and network fooling occurs
Solution Approach 1:
The patent implements a feedback mechanism where the trained 3D estimator neural network generates 3D shapes and textures, these are rendered into synthetic images, and the synthetic images are fed back into the network for further training. This closed-loop feedback process continuously improves estimation accuracy by using the network's own outputs as training data, thereby resolving the contradiction between accuracy and training complexity.
Solution Approach 2:
The system employs self-service by generating its own training data through rendering synthetic images from estimated 3D shapes and textures. The network trains itself iteratively using auto-supervised learning, where it creates its own supervision signals without requiring external annotated 3D data, thus improving accuracy while maintaining training simplicity.
2Measurement precision
If the neural network is trained to match image features, then feature accuracy is improved, but network fooling occurs where outputs visually differ from the original image
Solution Approach 1:
The patent resolves this contradiction by adding another dimension of verification - instead of relying solely on 2D image feature matching, the system renders the estimated 3D shapes and textures into synthetic 2D images and compares these rendered views with the original image. This multi-dimensional approach ensures both feature accuracy and visual reliability by validating the 3D estimates through multiple rendered perspectives.
Solution Approach 2:
The training process is segmented into multiple independent loss components: image feature loss, rendering loss, and shape/texture consistency loss. Each segment addresses a specific aspect of accuracy, and their combination ensures both image feature matching and visual resemblance without allowing network fooling to occur.
3Measurement precision
If multiple views are generated for training, then network fooling is mitigated and accuracy improves, but training time and computational resources increase
Solution Approach 1:
The patent applies partial action by selectively generating multiple views only when necessary for training iterations, rather than continuously generating all possible views. The system balances the number of rendered views against training time constraints, using just enough multi-view data to mitigate network fooling while avoiding excessive computational overhead.
Data Source
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AI summary
Methods, systems, and apparatus for obtaining first image features derived from an image of an object, providing the first image features to a three-dimensional estimator neural network, and obtaining, from the neural network, data specifying an estimated three-dimensional shape and texture based on the first image features. The estimated three-dimensional shape and texture are provided to a rendering engine, and a plurality of three-dimensional views of the object are generated by the rendering engine based on the estimated three-dimensional shape and texture. The three-dimensional views are provided to the object recognition engine, and second image features derived from the three-dimensional views are obtained from the object recognition engine. A loss is computed based at least on the first and second image features, and the neural network is trained based at least on the computed loss.