Gaussian Splat Capture With Priority-Based Viewpoint Sampling
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Solution Overview
Problem
Gaussian splat models have large file sizes and require improvements in memory usage and training speed.
Innovation Solution
Determine the priority of object portions, obtain images with varying concentrations of viewpoints based on priority, and use a training algorithm with dynamic loss and learning rates to generate and refine Gaussian splats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Gaussian splat models store detailed information for all object portions, then model accuracy is improved, but memory usage increases
Solution Approach 1:
The patent applies local quality by differentiating the storage precision of Gaussian splats based on the visual importance of object portions. High-priority portions (e.g., product front face) use full-precision Gaussian splats with complete data channels, while low-priority portions (e.g., backside, frame) use compressed representations with reduced data channels. This selective precision maintains model accuracy for critical areas while significantly reducing overall memory consumption.
Solution Approach 2:
The patent segments the object surface into multiple portions based on viewing frequency and visual importance. Each segment is then processed independently with appropriate precision levels. The system divides the object into high-priority and low-priority segments, applying different storage strategies to each, thereby optimizing the balance between accuracy and memory efficiency.
2Reliability
If the training algorithm processes all images with uniform concentration, then training completeness is improved, but training time increases
Solution Approach 1:
The training algorithm applies local quality by processing images with different concentration levels based on the priority of object portions they contain. Images showing high-priority portions are processed with higher concentration (more detailed Gaussian splat generation), while images showing low-priority portions use lower concentration. This selective processing maintains training completeness for critical areas while reducing overall training time.
Solution Approach 2:
The patent applies partial action by selectively processing only the necessary portion of training data with full detail. Instead of uniformly processing all images at maximum concentration, the system processes only high-priority portions at full concentration and accepts approximate representations for low-priority portions, achieving sufficient training effectiveness with reduced computational cost.
Data Source
AI summary
The present disclosure is related to a system and method for generating a three-dimensional (3D) model of an object. The method includes: determining that a first portion of an object has a lower priority; obtaining a plurality of images of the object; and training a three-dimensional model of the object using a training algorithm that processes the plurality of images as training data to generate and refine Gaussian splats defining the three-dimensional model. Obtaining the plurality of images includes obtaining images including views of the first portion of the object from a first concentration of viewpoints; and obtaining images including views of the another portion of the object from a second concentration of viewpoints, the first concentration being less than the second concentration.