A three-dimensional data encoding method includes: extracting, from first three-dimensional data, second three-dimensional data having an amount of a feature greater than or equal to a threshold; and encoding the second three-dimensional data to generate first encoded three-dimensional data. For example, the three-dimensional data encoding method may further include encoding the first three-dimensional data to generate the second encoded three-dimensional data.

Techniques to facilitate compression of depth data and real-time reconstruction of high-quality light fields. A parameter space of values for a line, pairs of endpoints on different sides of the line, and a palette index for each pixel of a pixel tile of a depth image is sampled. Values for the line, the pairs of endpoints, and the palette index that minimize an error are determined and stored.

Techniques to facilitate compression of depth data and real-time reconstruction of high-quality light fields. A parameter space of values for a line, pairs of endpoints on different sides of the line, and a palette index for each pixel of a pixel tile of a depth image is sampled. Values for the line, the pairs of endpoints, and the palette index that minimize an error are determined and stored.

The current document is directed to a dimensional shuffle transform (“DST”) that maps a 3D space to a one-dimensional space that preserves 3D neighborhoods within 1D neighborhoods within an implicit recursive hierarchical structure. The search for points in a 3D subspace is reduced, by the DST, to one or more searches in the transformed 1D space. The search is performed by either recursive decomposition of the 3D region indexed by the transform into subspaces, exploiting the transformed space structure, or by direct indexing into the region of interest. The searches over the subspaces generated by recursive decomposition are independent from one another, providing many opportunities for a variety of parallel, DST-enabled search methods.

The current document is directed to a dimensional shuffle transform (“DST”) that maps a 3D space to a one-dimensional space that preserves 3D neighborhoods within 1D neighborhoods within an implicit recursive hierarchical structure. The search for points in a 3D subspace is reduced, by the DST, to one or more searches in the transformed 1D space. The search is performed by either recursive decomposition of the 3D region indexed by the transform into subspaces, exploiting the transformed space structure, or by direct indexing into the region of interest. The searches over the subspaces generated by recursive decomposition are independent from one another, providing many opportunities for a variety of parallel, DST-enabled search methods.

A method for coding information of a point cloud comprises obtaining the point cloud including a set of points in a three-dimensional space; partitioning the point cloud into a plurality of objects and generating occupancy information for each of the plurality of objects; and encoding the occupancy information by taking into account the distance between the plurality of objects.

A method for coding information of a point cloud comprises obtaining the point cloud including a set of points in a three-dimensional space; partitioning the point cloud into a plurality of objects and generating occupancy information for each of the plurality of objects; and encoding the occupancy information by taking into account the distance between the plurality of objects.

A method and device for decoding a point cloud using octree partitioning and a predictive tree include obtaining the point cloud. A bounding box of the point cloud is determined. Octree nodes are generated by partitioning the bounding box using octree partitioning. The predictive tree is generated for points in at least one octree node of the octree nodes. A transform is applied to the predictive tree. The points in the at least one octree node are decoded using the predictive tree.

A method and device for decoding a point cloud using octree partitioning and a predictive tree include obtaining the point cloud. A bounding box of the point cloud is determined. Octree nodes are generated by partitioning the bounding box using octree partitioning. The predictive tree is generated for points in at least one octree node of the octree nodes. A transform is applied to the predictive tree. The points in the at least one octree node are decoded using the predictive tree.

A point cloud data transmission method according to embodiments comprises the steps of: encoding point cloud data; and transmitting signaling data and the encoded point cloud data, wherein the step for encoding may comprise the steps of: dividing the point cloud data into a plurality of compression units; sorting, for each compression unit, the point cloud data in each compression unit; generating a prediction tree on the basis of the sorted point cloud data in the compression units; and compressing the point cloud data in the compression units by predicting on the basis of the prediction tree.