NeRF Training Acceleration via Depth Map Preprocessing
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Solution Overview
Problem
Training neural radiance field (NeRF)-based machine learning models for facial recognition is laborious and time-consuming, making them unsuitable for commercial applications due to the extensive time required, which can take multiple weeks.
Innovation Solution
Preprocessing training data by determining depth maps and generating reconstructed content items and optimal training paths using dissimilarity matrices to accelerate the training process, allowing for parallel training and reducing the time needed to train the NeRF-based machine learning model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If NeRF-based machine learning models are trained using conventional techniques, then high-quality surface reconstruction with smooth and continuous surfaces is achieved, but training time becomes excessively long (multiple weeks)
Solution Approach 1:
The patent applies preliminary action by pre-processing the training data before actual model training. Depth maps are determined from content items, and reconstructed content items are generated in advance. This preprocessing step organizes and prepares the data structure beforehand, so that when training begins, the model can learn more efficiently without having to process raw unstructured data during the training process itself.
Solution Approach 2:
The patent segments the training process into distinct phases: first determining depth maps from content items, then generating reconstructed content items based on those depth maps, and finally training the model on the pre-processed reconstructed data. This segmentation allows each phase to be optimized independently, with the preprocessing phase preparing data in a format that accelerates the subsequent training phase while maintaining reconstruction quality.
2Reliability
If NeRF-based machine learning models are trained with comprehensive training data, then model accuracy is improved, but computing resource requirements and training time increase significantly
Solution Approach 1:
The patent extracts only the essential and relevant features from the original content items by determining depth maps and generating reconstructed content items. Instead of training on all original data including redundant information, the system extracts the critical depth and structural information needed for accurate reconstruction, removing unnecessary data that would otherwise consume computing resources during training.
Solution Approach 2:
The patent transforms the training data from original content items into a different parameter space by converting images into depth maps and then into reconstructed content items. This parameter transformation changes the data representation to a form that is more suitable for NeRF training, maintaining accuracy while improving training efficiency through better-suited data characteristics.
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media are configured to obtain a set of content items to train a neural radiance field-based (NeRF-based) machine learning model for object recognition. Depth maps of objects depicted in the set of content items can be determined. A first set of training data comprising reconstructed content items depicting only the objects can be generated based on the depth maps. A second set of training data comprising one or more optimal training paths associated with the set of content items can be generated based on the depth maps. The one or more optimal training paths are generated based at least in part on a dissimilarity matrix associated with the set of content items. The NeRF-based machine learning model can be trained based on the first set of training data and the second set of training data.