3D Asset Splatting With Adaptive Virtual Camera Sampling
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Solution Overview
Problem
Existing Gaussian splatting methods result in uneven sampling of 3D assets due to equal placement of virtual cameras, leading to inconsistent detail and obscured regions, which can cause important details to be lost and prevent accurate reconstruction.
Innovation Solution
A controlled sampling method using dynamic non-uniform placement of micro virtual cameras based on primitive priorities, ensuring complete and varied coverage by projecting rays from 3D asset primitives, capturing images from multiple angles, and combining them into a mosaic image for accurate splat representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If virtual cameras are uniformly placed around the 3D asset, then the camera placement is simple and consistent, but the sampling of the 3D asset becomes uneven due to surface curvature and occlusion
Solution Approach 1:
The patent applies local quality by differentiating camera placement strategies based on local surface properties. High-priority primitives with curved surfaces, specular materials, or high-frequency textures receive additional micro virtual cameras for multi-angle sampling, while low-priority flat matte surfaces are captured from fewer views. This localized adaptation of sampling density resolves the contradiction between simple uniform placement and accurate sampling.
Solution Approach 2:
The patent introduces dynamics by making the camera placement adaptive rather than static. The system dynamically determines the number and positions of micro virtual cameras based on primitive priority assessments, surface curvature, material properties, and texture frequency. This dynamic adjustment allows the system to optimize sampling accuracy for complex regions while maintaining simplicity for straightforward regions.
2Ease of manufacture
If uniform camera placement is used, then the processing is straightforward, but important details in curved or specular surfaces are lost due to insufficient sampling angles
Solution Approach 1:
The patent applies local quality by differentiating camera placement strategies based on local surface properties. High-priority primitives with curved surfaces, specular materials, or high-frequency textures receive additional micro virtual cameras for multi-angle sampling, while low-priority flat matte surfaces are captured from fewer views. This localized adaptation of sampling density resolves the contradiction between simple uniform placement and accurate sampling.
Solution Approach 2:
The patent applies preliminary action by performing a priority assessment of all primitives before finalizing camera placement. The system pre-identifies which surfaces require enhanced sampling based on their geometric and material properties, then allocates micro virtual cameras accordingly. This preliminary classification ensures that detail-critical regions are captured with sufficient angular diversity before the actual rendering process begins.
3Measurement precision
If more micro virtual cameras are placed to capture detailed regions, then the sampling coverage improves, but the system complexity and computational load increase
Solution Approach 1:
The patent applies local quality by differentiating camera placement strategies based on local surface properties. High-priority primitives with curved surfaces, specular materials, or high-frequency textures receive additional micro virtual cameras for multi-angle sampling, while low-priority flat matte surfaces are captured from fewer views. This localized adaptation of sampling density resolves the contradiction between simple uniform placement and accurate sampling.
Solution Approach 2:
The patent applies partial action by applying enhanced sampling only to the subset of primitives that require it, rather than uniformly oversampling all surfaces. The system performs partial action on high-priority regions (curved, specular, high-frequency texture areas) while using minimal sampling on low-priority regions. This selective approach achieves comprehensive coverage where needed without unnecessarily increasing system complexity for regions that don't require it.
4Stability of the object's composition
If uniform sampling is applied to all surfaces, then the process is consistent, but surfaces with varying detail requirements receive inadequate or excessive sampling
Solution Approach 1:
The patent applies local quality by differentiating camera placement strategies based on local surface properties. High-priority primitives with curved surfaces, specular materials, or high-frequency textures receive additional micro virtual cameras for multi-angle sampling, while low-priority flat matte surfaces are captured from fewer views. This localized adaptation of sampling density resolves the contradiction between simple uniform placement and accurate sampling.
Solution Approach 2:
The patent applies parameter changes by adjusting the sampling parameters (number of micro virtual cameras, angular distribution) based on primitive priority characteristics. The system modifies sampling density, camera angles, and view diversity according to surface curvature, material specularity, and texture frequency parameters. This parameter adaptation allows consistent processing workflows while achieving variable sampling fidelity matched to actual surface requirements.
Data Source
AI summary
A splat generation system and associated methods perform splatting based on a controlled sampling of a three-dimensional (3D) asset. The controlled sampling ensures minimal coverage the 3D asset primitives and provides enhanced coverage for primitives that have greater detail or variation. The system projects rays in different directions from a set of the primitives towards a bounding volume, and detects points at which some of the projected rays intersect the bounding volume. The system defines virtual cameras in the 3D space of the 3D asset based on a set of the intersection points, and obtains the minimal and/or enhanced coverage based on one or more views of the primitives captured in images taken by the virtual cameras. The system generates a set of splats from the one or more views in the captured images with an acceptable amount of loss and with less total data than the 3D asset.