1 research outputs found
Direct Fitting of Gaussian Mixture Models
When fitting Gaussian Mixture Models to 3D geometry, the model is typically
fit to point clouds, even when the shapes were obtained as 3D meshes. Here we
present a formulation for fitting Gaussian Mixture Models (GMMs) directly to a
triangular mesh instead of using points sampled from its surface. Part of this
work analyzes a general formulation for evaluating likelihood of geometric
objects. This modification enables fitting higher-quality GMMs under a wider
range of initialization conditions. Additionally, models obtained from this
fitting method are shown to produce an improvement in 3D registration for both
meshes and RGB-D frames. This result is general and applicable to arbitrary
geometric objects, including representing uncertainty from sensor measurements.Comment: Accepted to the Conference on Computer and Robot Vision 2019. 8 page