NeRF Training with Foreground-Biased Pixel Sampling
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Solution Overview
Problem
Neural Radiance Fields (NeRFs) require extensive training time and sparse sampling degrades the quality of 3D scene representation, while uniform sampling of pixels and voxels leads to computational inefficiencies and reduced accuracy.
Innovation Solution
Train NeRFs using a non-uniform sample of pixels that overrepresent foreground pixels relative to background pixels, leveraging image segmentation and signed distance functions to guide the sampling process, allowing the network to focus on areas of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If uniform sampling of pixels and voxels is used for training, then the training computational efficiency is improved, but the quality of 3D scene representation deteriorates
Solution Approach 1:
The patent applies local quality by differentiating between foreground and background regions in the training process. Foreground pixels (those corresponding to actual scene objects) are sampled at a higher rate than background pixels. This non-uniform sampling strategy concentrates computational resources on regions that contribute most to accurate 3D scene representation, thereby resolving the contradiction between training efficiency and representation quality.
2Loss of time
If sparse sampling of pixels and voxels is used for training, then the training time is reduced, but the accuracy of novel view synthesis deteriorates
Solution Approach 1:
The patent implements local quality through non-uniform sampling that prioritizes foreground pixels over background pixels during training. By allocating more samples to foreground regions (which contain the actual scene content) and fewer to background regions, the method achieves accurate novel view synthesis with reduced overall sampling density, thus reducing training time while maintaining accuracy.
Solution Approach 2:
The patent applies partial action by selectively sampling only the necessary portions of the image space for effective training. Instead of uniformly sampling all pixels, the method focuses computational effort on foreground pixels that contain meaningful scene information, achieving sufficient training accuracy with fewer total samples and reduced training time.
3Reliability
If every possible pixel and voxel is sampled for training, then the completeness of scene representation is improved, but the computational complexity increases excessively
Solution Approach 1:
The patent resolves this contradiction by applying local quality through non-uniform sampling that concentrates computational resources on foreground pixels while reducing sampling density in background regions. This approach maintains complete and accurate scene representation in critical areas without the excessive computational complexity of uniform full-resolution sampling.
Solution Approach 2:
The patent extracts and separates foreground pixels from background pixels, then applies different sampling densities to each category. By extracting the meaningful foreground content and treating it differently from the background, the method achieves complete scene representation without the computational burden of uniformly sampling every possible pixel and voxel.
Data Source
AI summary
Systems, methods, software, and devices are disclosed herein for training a neural network using multiple images of a scene captured from different viewing directions. The training is expedited by first determining foreground pixels in each of the multiple images, and then selecting a non-uniform sample of pixels from each of the multiple images such that the foreground pixels are overrepresented in the non-uniform sample of pixels relative to background pixels.The neural network may then be trained using radiance values of voxels on rays propagating from each of the non-uniform sample of pixels along a corresponding viewing direction into the scene.