A three-dimensional model ray tracing method based on meta learning
By employing a meta-learning-based ray tracing method, and utilizing stereo matching convolutional neural networks and loss functions to train the model, the problems of long training time and large data volume in ray tracing technology are solved, thereby improving the lighting effects and realism of 3D models.
CN115546383BActive Publication Date: 2026-04-07ZHONGKE (XIAMEN) DATA INTELLIGENCE RES INST
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Patent Information
- Application Number
- CN202211373309.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-11-04
- Publication Date
- 2026-04-07
- Estimated Expiration
- 2042-11-04
AI Technical Summary
Technical Problem
Existing ray tracing technology has excessively long training times, excessively large training data volumes, and low accuracy in ray tracing, resulting in poor lighting and shadow effects in 3D models.
Method used
We employ a meta-learning approach, constructing a general ray tracing model, training an old model using a stereo matching convolutional neural network, and jointly training it using cross-entropy loss and knowledge distillation loss functions. By removing some data as memory data, we train a personalized model that adapts to specific ray tracing effects.
Benefits of technology
It shortened the training time, reduced the data volume, and improved the accuracy of both new and old data, thereby enhancing the lighting and realism of the 3D model.
✦ Generated by Eureka AI based on patent content.
Abstract
The application discloses a three-dimensional model light ray tracing method based on meta learning, and belongs to the technical field of three-dimensional model image processing, and comprises the following steps: step one, constructing a general model for light ray tracing; step two, training a previous model by using a stereo matching convolutional neural network; step three, jointly training the previous model to obtain a new model; and step four, finding customized parameters and a customized model of the new model meeting a specific light ray tracing effect.The application trains an old model by using a stereo matching convolutional neural network based on the construction of a general model, trains general parameters meeting a specific light tracing effect, obtains new potential general parameters through the convergence of a loss function, adapts to a specific light tracing effect, effectively trains the old model by removing 90% of previous data and 98% of previous data, reduces the training data capacity, improves the accuracy of new and old data, and thus solves the problems of long training time, large training data capacity and low light ray tracing effect accuracy of the light ray tracing technology.
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