Three-dimensional object detection using state-space spatiotemporal learning and dynamic queries

An apparatus for 3D object detection, comprising:

• one or more memories;
• and
• one or more processors coupled to the one or more memories, the one or more processors being configured to:
• filter an obtained set of proposal pillars and set of proposal features associated with the set of proposal pillars to obtain a set of sampling points;
• sample features from a set of images based on the set of sampling points;
• mask a random set of features from the sampled features to generate a masked set of features;
• generate, using a state space model, a state-space representation of the features based on the masked set of features and a predicted set of features;
• mix the state-space representation of the features to generate mixed features;
• identify a set of bounding boxes associated with objects in the set of images based on the mixed features;
• generate classifications for the objects in the set of images based on the mixed features;
• and
• output the set of bounding boxes and classifications.