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We present a method to compress geometry information of point clouds that explores redundancies across consecutive frames of a sequence. It uses octrees and works by progressively increasing the resolution of the octree. At each branch of the tree, we generate an approximation of the child nodes by a number of methods which are used as contexts to drive an arithmetic coder. The best approximation, i.e., the context that yields the least amount of encoding bits, is selected and the chosen method is indicated as side information for replication at the decoder. The core of our method is a context-based arithmetic coder in which a reference octree is used as a reference to encode the current octree, thus providing 255 contexts for each output octet. The 255 × 255 frequency histogram is viewed as a discrete 3D surface and is conveyed to the decoder using another octree. We present two methods to generate the predictions (contexts) which use adjacent frames in the sequence (inter-frame) and one method that works purely intra-frame. The encoder continuously switches the best mode among the three and conveys such information to the decoder. Since an intra-frame prediction is present, our coder can also work in purely intra-frame mode, as well. Extensive results are presented to show the method's potential against many compression alternatives for the geometry information in dynamic voxelized point clouds.
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