CPC G06T 17/10 (2013.01) [G06N 3/045 (2023.01); G06N 3/084 (2013.01); G06T 19/20 (2013.01); G06T 2219/2016 (2013.01)] | 20 Claims |
1. A computer-implemented method for training a machine learning model to generate representations of point clouds, the method comprising:
executing a first neural network on a first point cloud that represents a first three-dimensional (3D) scene to generate a key set and a value set;
generating an output vector set based on a first query set, the key set, and the value set;
computing a plurality of spatial features based on the output vector set;
computing a plurality of quantized context features based on the output vector set and a first set of codes representing a first set of 3D geometry blocks; and
modifying the first neural network based on a likelihood of reconstructing the first point cloud, the plurality of quantized context features, and the plurality of spatial features to generate an updated neural network,
wherein a trained machine learning model includes the updated neural network, a second query set, and a second set of codes representing a second set of 3D geometry blocks and maps a point cloud representing a 3D scene to a representation of a plurality of 3D geometry instances.
|