Company Filing History:
Years Active: 2019-2020
Title: Innovations of Benjamin David Eckart
Introduction
Benjamin David Eckart is a notable inventor based in Pittsburgh, PA (US). He has made significant contributions to the field of 3D perception, particularly in the areas of point cloud registration and Gaussian mixture models. With a total of 2 patents, Eckart's work is at the forefront of technological advancements in autonomous navigation and augmented reality.
Latest Patents
Eckart's latest patents include "Fast multi-scale point cloud registration with a hierarchical gaussian mixture" and "Modeling point cloud data using hierarchies of Gaussian mixture models." The first patent presents a new registration algorithm that enhances speed and accuracy in registering point clouds, which is crucial for various applications such as autonomous navigation and object recognition. The algorithm utilizes an Expectation-Maximization (EM) approach to associate points in the target point cloud with nodes in a hierarchical tree structure, ultimately determining an estimated transformation through a minimization problem related to Mahalanobis distance. The second patent outlines a method for generating a Gaussian mixture model hierarchy, which involves defining mixels that encode parameters for probabilistic occupancy maps and adjusting these parameters based on point cloud data through iterations of the EM algorithm.
Career Highlights
Eckart is currently employed at Nvidia Corporation, a leading company in graphics processing and AI technology. His work at Nvidia allows him to apply his innovative ideas in a dynamic and impactful environment.
Collaborations
Some of his notable coworkers include Kihwan Kim and Jan Kautz, who contribute to the collaborative efforts in advancing technology at Nvidia.
Conclusion
Benjamin David Eckart's contributions to the field of 3D perception through his patents and work at Nvidia Corporation highlight his role as a significant innovator in the industry. His advancements in point cloud registration and Gaussian mixture models are paving the way for future developments in autonomous systems and augmented reality.