NeRF Training With Spatially Dynamic Loss for Sparse Scene Sampling
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Solution Overview
Problem
Neural radiance fields (NeRFs) require extensive training time and sparse sampling degrades their ability to accurately represent 3D scenes, leading to reduced quality in rendered images.
Innovation Solution
Train NeRFs using multiple images from different viewing directions with a dynamic loss function that varies based on spatial features like distance to the object surface and foreground probabilities, focusing training on critical scene regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution sampling of all pixels and voxels is performed, then the accuracy of NeRF representation is improved, but the training time and computational cost increase excessively
Solution Approach 1:
The patent applies local quality by using spatially varying loss terms that apply different weighting to different regions of the scene. Specifically, loss terms are modulated by spatial coordinates to emphasize regions closer to the camera or regions with higher importance, allowing the network to focus computational effort on critical areas rather than uniformly processing all voxels with equal weight.
Solution Approach 2:
The patent implements dynamics by making the loss function adaptive and variable during training. The loss terms are dynamically adjusted based on spatial position, allowing the training process to adaptively focus on different regions at different stages. This dynamic weighting scheme enables the system to prioritize learning in important regions while reducing computational burden in less critical areas.
2Productivity
If sparse sampling of pixels and voxels is performed, then the computational efficiency of training is improved, but the quality of the trained NeRF and its ability to accurately represent a 3D scene degrades
Solution Approach 1:
The patent applies local quality by using spatially varying loss terms that apply different weighting to different regions of the scene. Specifically, loss terms are modulated by spatial coordinates to emphasize regions closer to the camera or regions with higher importance, allowing the network to focus computational effort on critical areas rather than uniformly processing all voxels with equal weight.
Solution Approach 2:
The patent implements parameter changes by modifying the loss function parameters to include spatial dependencies. The loss terms are changed from uniform weights to spatially varying weights that depend on voxel position, camera position, and other parameters. This allows the same sparse sampling strategy to produce different effective training densities in different regions, improving overall quality without increasing computational cost.
3Ease of manufacture
If uniform sampling of pixels and voxels is performed, then the training process is simplified, but the training focuses equally on all regions including less important background areas, reducing efficiency
Solution Approach 1:
The patent applies local quality by using spatially varying loss terms that apply different weighting to different regions of the scene. Specifically, loss terms are modulated by spatial coordinates to emphasize regions closer to the camera or regions with higher importance, allowing the network to focus computational effort on critical areas rather than uniformly processing all voxels with equal weight.
Solution Approach 2:
The patent implements dynamics by making the loss function adaptive and variable during training. The loss terms are dynamically adjusted based on spatial position, allowing the training process to adaptively focus on different regions at different stages. This dynamic weighting scheme enables the system to prioritize learning in important regions while reducing computational burden in less critical areas.
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. A method of training the network includes identifying pixels in multiple images of a scene captured from different viewing directions. and determining, for each of the pixels, at least a known color value, a known radiance value, and a spatial value. The training minimizes a loss function having multiple loss terms: a first loss term that is dependent upon at least the known color value and the known radiance value for each of the pixels; and one or more additional loss terms dependent upon the spatial value determined for each pixel, such that the loss function varies for at least some of the pixels.