3D Splat Generation Using Loss-Weighted Image Sampling
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Solution Overview
Problem
Existing Gaussian splatting methods treat all images equally during training, leading to disproportionate file size increases and wasted processing cycles due to unnecessary iterations on low complexity regions, resulting in imperceptible detail improvements.
Innovation Solution
Implement loss weighted image sampling to prioritize training on images with higher reconstruction loss, adjusting the selection frequency based on image complexity and loss values to generate non-uniform splat representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If equal sampling of all images is used during training, then uniform detail is achieved across all surfaces, but file size increases disproportionately and processing time is wasted on low complexity regions
Solution Approach 1:
The patent applies local quality by differentiating training intensity based on region complexity. High complexity regions receive more training iterations and higher sampling weights, while low complexity regions receive fewer iterations and lower weights. This is achieved through computing loss values for each image and using these losses to weight the selection probability in subsequent training iterations, thereby allocating computational resources non-uniformly according to local needs.
Solution Approach 2:
The patent implements partial action by performing training iterations selectively rather than uniformly on all images. Images with lower loss values (indicating better reconstruction) are sampled less frequently or excluded from certain iterations, while images with higher loss values are sampled more frequently. This partial application of training action reduces overall processing time while maintaining acceptable fidelity for the majority of the scene.
2Manufacturing precision
If equal sampling of all images is used during training, then uniform detail is achieved across all surfaces, but file size increases disproportionately
Solution Approach 1:
The patent applies local quality by differentiating training intensity based on region complexity. High complexity regions receive more training iterations and higher sampling weights, while low complexity regions receive fewer iterations and lower weights. This is achieved through computing loss values for each image and using these losses to weight the selection probability in subsequent training iterations, thereby allocating computational resources non-uniformly according to local needs.
Solution Approach 2:
The patent implements partial action by performing training iterations selectively rather than uniformly on all images. Images with lower loss values (indicating better reconstruction) are sampled less frequently or excluded from certain iterations, while images with higher loss values are sampled more frequently. This partial application of training action reduces overall processing time while maintaining acceptable fidelity for the majority of the scene.
3Reliability
If all images are selected the same number of times, then consistent training coverage is achieved, but processing cycles are wasted on already reconstructed regions
Solution Approach 1:
The patent implements feedback by using the loss computed from each image's reconstruction quality to influence the selection probability for subsequent training iterations. Images with higher loss values (poorer reconstruction) are assigned higher weights and are more likely to be selected for future training, while images with lower loss values are assigned lower weights. This feedback loop dynamically adjusts training coverage based on actual reconstruction performance, improving both reliability and productivity.
Solution Approach 2:
The patent applies dynamics by making the image selection process adaptive rather than static. The sampling weights are updated iteratively based on observed loss values, allowing the training process to dynamically shift focus toward regions that need more attention. This dynamic adjustment enables the system to automatically adapt to the varying complexity and importance of different regions in the scene.
Data Source
AI summary
A splat generation system and associated methods generate a non-uniform splat representation of a three-dimensional (3D) asset based on a loss weighted sampling of reference images that capture the 3D asset from different viewpoints. The system performs a first training iteration to define a different set of splats to reconstruct the field-of-view captured by a different one of the reference images. The system associates an amount of loss to each reference image based on an amount of variation by which a set of splats trained on that reference image reconstructs the field-of-view of that reference image. The system selects a next image to train on based on the amount of loss associated with each of the reference images, and retrains the set of splats representing the field-of-view of the selected next image by adjusting one or more of those splats to increase the reconstructed field-of-view accuracy.