Dense three-dimensional correspondence estimation with multi-level metric learning and hierarchical matching

A non-transitory computer-readable storage medium comprising a computer-readable program for estimating dense three-dimensional (3D) geometric correspondences between two input point clouds by employing a 3D convolutional neural network (CNN) architecture, wherein the computer-readable program when executed on a computer causes the computer to perform the steps of:

• 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 to obtain unit vector features;
• and
• applying deep supervision of multiple contrastive losses and multiple hard negative mining modules at the first and second feature extraction layers;
• and
• 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.

A system for estimating dense three-dimensional (3D) geometric correspondences between two input point clouds by employing a 3D convolutional neural network (CNN) architecture, the system comprising:

• a training module for training by:
• 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 to obtain unit vector features;
• and
• applying deep supervision of multiple contrastive losses and multiple hard negative mining modules at the first and second feature extraction layers;
• and
• a testing module for testing by:
• 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.