GaMeS: Mesh-Based Adapting and Modification of Gaussian Splatting
CoRR(2024)
摘要
In recent years, a range of neural network-based methods for image rendering
have been introduced. For instance, widely-researched neural radiance fields
(NeRF) rely on a neural network to represent 3D scenes, allowing for realistic
view synthesis from a small number of 2D images. However, most NeRF models are
constrained by long training and inference times. In comparison, Gaussian
Splatting (GS) is a novel, state-of-theart technique for rendering points in a
3D scene by approximating their contribution to image pixels through Gaussian
distributions, warranting fast training and swift, real-time rendering. A
drawback of GS is the absence of a well-defined approach for its conditioning
due to the necessity to condition several hundred thousand Gaussian components.
To solve this, we introduce Gaussian Mesh Splatting (GaMeS) model, a hybrid of
mesh and a Gaussian distribution, that pin all Gaussians splats on the object
surface (mesh). The unique contribution of our methods is defining Gaussian
splats solely based on their location on the mesh, allowing for automatic
adjustments in position, scale, and rotation during animation. As a result, we
obtain high-quality renders in the real-time generation of high-quality views.
Furthermore, we demonstrate that in the absence of a predefined mesh, it is
possible to fine-tune the initial mesh during the learning process.
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