3D Gaussian Splat Initialization via Trained Neural Radiance Fields
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Solution Overview
Problem
The generation of three-dimensional gaussian splat (3DGS) representations is slow due to inefficient initialization methods, leading to long training times and potential failure to converge, especially when compared to neural radiance field (NeRF) representations which can be trained quickly but render slowly.
Innovation Solution
Utilizing a trained neural radiance field representation to seed gaussian splats with correct positions, densities, and colors, allowing for rapid initialization and convergence of the 3DGS algorithm by retracing training rays and generating splats near scene geometry, thereby reducing overall training time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional initialization methods are used for 3DGS representations, then the algorithm can be implemented, but the training time is excessively long and convergence is unreliable
Solution Approach 1:
The patent applies preliminary action by using a pre-trained neural radiance field (NeRF) model to generate initial 3D gaussian splats before the main 3DGS training process. The NeRF model is trained first to capture scene geometry and appearance, then its predictions are used to initialize gaussian positions, densities, and colors. This preliminary initialization provides a head start to the 3DGS optimization, making convergence more reliable and significantly reducing training time from hours to minutes.
2Productivity
If neural radiance field representations are used, then training is fast, but rendering speed is slow
Solution Approach 1:
The patent merges the advantages of both NeRF and 3DGS by using NeRF for fast training initialization and then transitioning to 3DGS for fast rendering. The hybrid approach combines NeRF's efficient training process with 3DGS's rapid rendering capability. After initializing gaussians using the pre-trained NeRF, the system performs brief 3DGS optimization to achieve a representation that renders quickly while maintaining training efficiency.
Data Source
AI summary
Example systems, apparatus, articles of manufacture, and methods are disclosed to implement three dimensional gaussian splatting initialization based on trained neural radiance field representations. Example apparatus disclosed herein determine a location for an initial three-dimensional (3D) gaussian splat based on optical densities obtained from a trained neural representation of a scene, the optical densities associated with location sample points along a training ray used to train the neural representation. Disclosed example apparatus also set parameters of the initial 3D gaussian splat based on one of the optical densities associated with the location of the initial 3D gaussian splat and a color value obtained from the trained neural representation, the color value associated with the location of the initial 3D gaussian splat, the initial 3D gaussian splat to be used to generate a 3D gaussian splat representation of the scene.