Learning based bitwise octree entropy coding compression and processing in light detection and ranging (lidar) and other systems

A device comprising:

• a processor;
• and
• a transceiver operatively coupled to the processor;
• wherein
• the processor and the transceiver are configured to access a set of occupancy bits of neighboring voxels at a current level of detail (LoD) that are already encoded or decoded, using a tree-based point cloud decoder;
• the processor is configured to compute a first feature, using a first neural network module upon a whole point cloud frame at the current LoD, based on the accessed set of occupancy bits of neighboring voxels at the current LoD, wherein the first neural network module is a convolution based neural network module;
• the processor is configured to access a second feature, wherein the second feature is a deep feature vector from a previous LoD;
• the processor is configured to combine the first feature and the second feature using a second neural network module upon the whole point cloud frame at the current LoD to obtain a third feature, wherein the second neural network module is a convolution based neural network module;
• the processor is configured to concatenate the third feature along with one or more features of the current voxels to be encoded or decoded to compose a fourth feature, wherein the fourth feature is a comprehensive feature;
• and
• the processor is configured to predict a probability distribution of occupancy of voxels at the current LoD based on the fourth feature using a third neural network module.