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Gaussian splatting model of a collapsed building taken from drone footage. 3D Gaussian splatting is a technique used in the field of real-time radiance field rendering. [3] It enables the creation of high-quality real-time novel-view scenes by combining multiple photos or videos, addressing a significant challenge in the field.
Gaussian splatting is a newer method that can outperform NeRF in render time and fidelity. Rather than representing the scene as a volumetric function, it uses a sparse cloud of 3D gaussians. First, a point cloud is generated (through structure from motion) and converted to gaussians of initial covariance, color, and opacity. The gaussians are ...
It is sometimes referred to as "4D Gaussian splatting"; however, this naming convention implies the use of 4D Gaussian primitives (parameterized by a 4×4 mean and a 4×4 covariance matrix). Most work in this area still employs 3D Gaussian primitives, applying temporal constraints as an extra parameter of optimization.
Example of texture splatting, except an additional alphamap is applied. In computer graphics, texture splatting is a method for combining different textures.It works by applying an alphamap (also called a "weightmap" or a "splat map") to the higher levels, thereby revealing the layers underneath where the alphamap is partially or completely transparent.
In scientific visualization and computer graphics, volume rendering is a set of techniques used to display a 2D projection of a 3D discretely sampled data set, typically a 3D scalar field. A typical 3D data set is a group of 2D slice images acquired by a CT , MRI , or MicroCT scanner .
Too lazy to, Aadirulez8, Muikuilani, and SafariScribe: I propose merging 3D Gaussian splatting into Gaussian splatting, and leaving 3D Gaussian splatting as a redirect. It is somewhat implied that in most cases, Gaussian Splatting is three dimensional.
Head and cerebral structures (hidden) extracted from 150 MRI slices using marching cubes (about 150,000 triangles). Marching cubes is a computer graphics algorithm, published in the 1987 SIGGRAPH proceedings by Lorensen and Cline, [1] for extracting a polygonal mesh of an isosurface from a three-dimensional discrete scalar field (the elements of which are sometimes called voxels).
By virtue of the linearity property of optical non-coherent imaging systems, i.e., . Image(Object 1 + Object 2) = Image(Object 1) + Image(Object 2). the image of an object in a microscope or telescope as a non-coherent imaging system can be computed by expressing the object-plane field as a weighted sum of 2D impulse functions, and then expressing the image plane field as a weighted sum of the ...