%20and%20the%20position%20(a%20classification%20problem)%20of%20occupied%20child%20nodes%20into%20a%20255-dimensional%20classification%20problem.%20As%20a%20result%2C%20it%20fails%20to%20accurately%20measure%20the%20difference%20between%20the%20one-hot%20encoding%20and%20the%20predicted%20probability%20distribution.%20We%20first%20analyze%20why%20the%20cross-entropy%20loss%20function%20fails%20to%20accurately%20measure%20the%20difference%20between%20the%20one-hot%20encoding%20and%20the%20predicted%20probability%20distribution.%20Then%2C%20we%20propose%20an%20attention-based%20child%20node%20number%20prediction%20(ACNP)%20module%20to%20enhance%20the%20context%20models.%20The%20proposed%20module%20can%20predict%20the%20number%20of%20occupied%20child%20nodes%20and%20map%20it%20into%20an%208-%20dimensional%20vector%20to%20assist%20the%20context%20model%20in%20predicting%20the%20probability%20distribution%20of%20the%20occupancy%20of%20the%20current%20node%20for%20efficient%20entropy%20coding.%20Experimental%20results%20demonstrate%20that%20the%20proposed%20module%20enhances%20the%20coding%20efficiency%20of%20octree-based%20context%20models.)
%20and%20a%20voxel-based%20model%20(VoxelDNN)%20on%20the%20object%20point%20cloud%20datasets%20MPEG%208i%20and%20MVUB%2C%20as%20well%20as%20the%20LiDAR%20point%20cloud%20dataset%20SemanticKITTI.)
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