Muhammad A Zia

US Patent:

20200058156, Feb 20, 2020

Filed:

Jul 30, 2019

Appl. No.:

16/526306

Inventors:

- Princeton NJ, US
Mohammed E. Fathy Salem - Hyattsville MD, US
Muhammad Zeeshan Zia - San Jose CA, US
Paul Vernaza - Sunnyvale CA, US
Manmohan Chandraker - Santa Clara CA, US

International Classification:

G06T 15/08
G06K 9/62
G06K 9/72
G06N 3/04
G06N 3/08
G06T 15/10

Abstract:

A method for estimating dense 3D geometric correspondences between two input point clouds by employing a 3D convolutional neural network (CNN) architecture is presented. The method includes, during a training phase, transforming the two input point clouds into truncated distance function voxel grid representations, feeding the truncated distance function voxel grid representations into individual feature extraction layers with tied weights, extracting low-level features from a first feature extraction layer, extracting high-level features from a second feature extraction layer, normalizing the extracted low-level features and high-level features, and applying deep supervision of multiple contrastive losses and multiple hard negative mining modules at the first and second feature extraction layers. The method further includes, during a testing phase, employing the high-level features capturing high-level semantic information to obtain coarse matching locations, and refining the coarse matching locations with the low-level features to capture low-level geometric information for estimating precise matching locations.

US Patent:

20190065868, Feb 28, 2019

Filed:

Jul 6, 2018

Appl. No.:

16/029167

Inventors:

- Princeton NJ, US
Mohammed E.F. Salem - Hyattsville MD, US
Muhammad Zeeshan Zia - San Jose CA, US
Paul Vernaza - Sunnyvale CA, US
Manmohan Chandraker - Santa Clara CA, US

International Classification:

G06K 9/00
G06K 9/62

Abstract:

Systems and methods for detecting traffic scenarios include an image capturing device which captures two or more images of an area of a traffic environment with each image having a different view of vehicles and a road in the traffic environment. A hierarchical feature extractor concurrently extracts features at multiple neural network layers from each of the images, with the features including geometric features and semantic features, and for estimating correspondences between semantic features for each of the images and refining the estimated correspondences with correspondences between the geometric features of each of the images to generate refined correspondence estimates. A traffic localization module uses the refined correspondence estimates to determine locations of vehicles in the environment in three dimensions to automatically determine a traffic scenario according to the locations of vehicles. A notification device generates a notification of the traffic scenario.

US Patent:

20190066373, Feb 28, 2019

Filed:

Jul 6, 2018

Appl. No.:

16/029126

Inventors:

- Princeton NJ, US
Mohammed E.F. Salem - Hyattsville MD, US
Muhammad Zeeshan Zia - San Jose CA, US
Paul Vernaza - Sunnyvale CA, US
Manmohan Chandraker - Santa Clara CA, US

International Classification:

G06T 17/05
G06K 9/62
G06K 9/46
G06K 9/66
G06N 3/08

Abstract:

Systems and methods for correspondence estimation and flexible ground modeling include communicating two-dimensional (2D) images of an environment to a correspondence estimation module, including a first image and a second image captured by an image capturing device. First features, including geometric features and semantic features, are hierarchically extract from the first image with a first convolutional neural network (CNN) according to activation map weights, and second features, including geometric features and semantic features, are hierarchically extracted from the second image with a second CNN according to the activation map weights. Correspondences between the first features and the second features are estimated, including hierarchical fusing of geometric correspondences and semantic correspondences. A 3-dimensional (3D) model of a terrain is estimated using the estimated correspondences belonging to the terrain surface. Relative locations of elements and objects in the environment are determined according to the 3D model of the terrain. A user is notified of the relative locations.