An example device for coding point cloud data includes a memory configured to store point cloud data; and one or more processors implemented in circuitry and configured to: determine whether inter prediction data is coded for a current node of an octree of the point cloud data; determine whether planar mask data is coded for the current node; when at least one of the inter prediction data or the planar mask data is coded for the current node, avoid coding a single occupancy value for the current node, the single occupancy value indicating whether only a single sub-node of the current node includes a point; and code the current node. The processors may also be configured to determine a context for entropy coding the planar mask data according to planar mask data for a collocated node in a reference frame when the planar mask data is coded.

Aspects of the disclosure provide methods and apparatuses for mesh coding (e.g., compression and decompression). In some examples, an apparatus for mesh coding includes processing circuitry. The processing circuitry decodes, using a point cloud compression (PCC) decoder and from a bitstream, a point cloud that includes points corresponding to at least one of vertices in a mesh or sampling points of polygons in the mesh. The processing circuitry determines, based on the point cloud, estimated connectivity information of the vertices in the mesh, and generate, a reconstructed mesh frame based on the point cloud and the estimated connectivity information.

A search space for performing nearest neighbor searches for encoding point cloud data may be trimmed. Ranges of a space filling curve may be used to identify search space to exclude or reuse, instead of generating nearest neighbor search results for at least some of the points of a point cloud located within some of the ranges of the space filling curve. Additionally, neighboring voxels may be searched to identify any neighboring points missed during the trimmed search based on the ranges of the space filling curve.

Disclosed herein is a method for compressing point cloud data. The method includes quantizing input point cloud data, generating a global motion matrix based on the quantized point cloud data, applying the global motion matrix based on whether local motion compression is performed, and compressing data to which the global motion matrix is applied.

A three-dimensional data encoding method is a three-dimensional data encoding method of encoding three-dimensional points, and includes: determining a predicted value, based on geometry information of one or more candidate points among encoded three-dimensional points; encoding a prediction residual which is a difference between the predicted value and geometry information of a current three-dimensional point to be encoded among the three-dimensional points; generating a bitstream including the prediction residual encoded; and determining the one or more candidate points, for inter prediction of the current three-dimensional point, based on a referenced three-dimensional point encoded immediately before the current three-dimensional point. The three-dimensional points constitute a prediction tree.

Disclosed is a data transmission system that transmits data by using a relay. The relay selects a transmission terminal from among a plurality of terminals accessing a base station. A base station transmits base station data to the relay during a first time slot, and the transmission terminal transmits terminal data to the relay. The relay transmits terminal data to the base station during a second time slot, and transmits base station data to the transmission terminal.

An encoder is configured to compress point cloud geometry information using a prediction tree. Ancestor nodes of a node added to the prediction tree may be used to determine the predicted value of the node to be added according to a prediction technique. The prediction tree may be encoded and may be provided for transmission to a decoder that can regenerate the point cloud.

A system comprises an encoder configured to compress attribute information and/or spatial for a point cloud and/or a decoder configured to decompress compressed attribute and/or spatial information for the point cloud. To compress the attribute and/or spatial information, the encoder is configured to convert a point cloud into an image based representation. Also, the decoder is configured to generate a decompressed point cloud based on an image based representation of a point cloud.

Aspects of the disclosure provide methods and apparatuses for point cloud compression and decompression. In some examples, an apparatus for point cloud compression/decompression includes processing circuitry. The processing circuitry determines to use a prediction mode for coding (encoding/decoding) information associated with a current point in a point cloud. In the prediction mode, the information associated with the current point is predicted based on one or more neighbor points of the current point. The processing circuitry calculates, using integer operations, a distance-based weighted average value based on distances of the one or more neighbor points to the current point, and determines the information associated with the current point based on the distance-based weighted average value.

A point cloud data transmission method according to embodiments may comprise the steps of: acquiring point cloud data; encoding geometry information of the point cloud data; encoding attribute information of the point cloud data on the basis of the geometry information; and transmitting a bitstream including the encoded geometry information, the encoded attribute information, and signaling information.