Optimizations for Dynamic Object Instance Detection, Segmentation, and Structure Mapping

A system comprising:

• one or more processors and one or more computer-readable non-transitory storage media coupled to one or more of the processors, the one or more computer-readable non-transitory storage media comprising instructions operable when executed by one or more of the processors to cause the system to perform operations comprising:
• accessing an image;
• generating a feature map for the image using a neural network;
• identifying a plurality of regions of interest in the feature map;
• generating a plurality of regional feature maps for the plurality of regions of interest, respectively, wherein each of the plurality of regional feature maps has a first dimension, a second dimension, and a third dimension;
• generating a first combined regional feature map by combining the plurality of regional feature maps, wherein the combined regional feature map has a first dimension, a second dimension, and a third dimension;
• generating a second combined regional feature map by processing the first combined regional feature map using one or more convolutional layers;
• and
• generating, for each of the plurality of regions of interest, information associated with an object instance based on a portion of the second combined regional feature map associated with that region of interest.

One or more computer-readable non-transitory storage media embodying software that is operable when executed to cause one or more processors to perform operations comprising:

• accessing an image;
• generating a feature map for the image using a neural network;
• identifying a plurality of regions of interest in the feature map;
• generating a plurality of regional feature maps for the plurality of regions of interest, respectively, wherein each of the plurality of regional feature maps has a first dimension, a second dimension, and a third dimension;
• generating a first combined regional feature map by combining the plurality of regional feature maps, wherein the combined regional feature map has a first dimension, a second dimension, and a third dimension;
• generating a second combined regional feature map by processing the first combined regional feature map using one or more convolutional layers;
• and
• generating, for each of the plurality of regions of interest, information associated with an object instance based on a portion of the second combined regional feature map associated with that region of interest.