3D Implicit Neural Representation Training With Geometric Priors
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Solution Overview
Problem
Traditional NeRF methods require extensive training time and struggle to integrate geometric priors effectively, leading to slow convergence and resource-intensive training processes, especially for human-manufactured objects.
Innovation Solution
Incorporates geometric priors by using a denoising neural network to accelerate the convergence of Laplacian features during training, leveraging the shared probability distribution of Laplacian images from synthetic 3D objects to guide the implicit neural representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional NeRF training methods are used, then the model can be trained without additional geometric constraints, but the training time is extensive and convergence is slow
Solution Approach 1:
The patent applies preliminary action by pre-computing and incorporating geometric priors (Laplacian features) from synthetic 3D objects before the main training process. This pre-processing step provides the model with initial geometric knowledge, accelerating convergence during actual training without sacrificing accuracy. The geometric constraints are prepared in advance and integrated into the training pipeline to guide the implicit neural representation toward accurate 3D reconstruction faster.
Solution Approach 2:
The patent introduces Laplacian features as an intermediary between the input images and the implicit neural representation. These geometric priors act as a mediator that translates image data into structured geometric constraints, guiding the training process more efficiently. The Laplacian features serve as an intermediate representation that bridges the gap between raw image data and the final 3D model, improving both convergence speed and accuracy.
2Manufacturing precision
If more training data is collected to improve model accuracy, then the 3D model precision improves, but the data processing requirements and computational resources increase substantially
Solution Approach 1:
The patent uses copying by synthesizing training data from virtual 3D objects and rendering them to create synthetic training images. Instead of collecting and processing大量 real-world images, the system creates copies of 3D objects in virtual environments with controlled lighting and camera positions. This synthetic data copying approach provides sufficient training examples without the computational burden of processing extensive real image datasets, while still achieving high 3D reconstruction accuracy.
Solution Approach 2:
The patent applies parameter changes by modifying the training approach to use Laplacian features as additional supervisory signals. Instead of relying solely on pixel-wise image matching, the system changes the training parameters to include geometric constraint terms based on Laplacian operators. This parameter modification allows the model to learn from fewer images by incorporating structural geometric information, reducing data processing requirements while maintaining or improving 3D model accuracy.
3Speed
If geometric priors are integrated into the training process, then convergence speed improves, but the device complexity and computational overhead increase
Solution Approach 1:
The patent applies universality by designing a training pipeline that handles both image-based supervision and geometric prior integration through a unified loss function. The same implicit neural representation model processes both photometric errors from images and geometric constraints from Laplacian features, making the system multi-functional without requiring separate processing pipelines. This universal approach integrates geometric priors efficiently, improving convergence speed while avoiding excessive complexity multiplication.
Data Source
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AI summary
A computer implemented method, comprising: acquiring a plurality of images of a first scene captured from different viewing positions; training a 3D implicit neural representation model for the scene using the acquired images; wherein the training comprises rendering an image from the 3D model of the scene, the image being rendered as if captured from a first viewing position, comparing the rendered image with one of the acquired images taken from the first viewing position, and adjusting one or more parameters of the model in dependence on a result of the comparison; and the training further comprises rendering a 2D spatial image bearing 3D information, the 2D spatial image being rendered as if captured from a second viewing position, and adjusting one or more parameters of the model in dependence on the 2D spatial image.