A method, computer program, and computer system is provided for point cloud coding. Data corresponding to a point cloud is received. Hash elements corresponding to attribute values associated with the received data is reconstructed. A size of a hash table may be decreased based on deleting one or more of the hash elements corresponding to non-border regions associated with the attribute values. The data corresponding to the point cloud is decoded based on the reconstructed hash elements.

There is included a method and apparatus comprising computer code configured to cause a processor or processors to perform obtaining a leaf node of geometry based point cloud compression (G-PCC) data, splitting the leaf node into a plurality of cuboids, deriving separate triangle soups for each of the cuboids, and coding a plurality of flags respectively for each of the edges of each of the cuboids, where the plurality of flags indicate whether vertices of the separate triangle soups are present on ones of the edges.

A method of interframe point cloud attribute coding is performed by at least one processor and includes coding a first color attribute of a point of a point cloud to obtain a first reconstructed residual, coding a second color attribute of the point to obtain a second reconstructed residual, and determining a quantization index of the second reconstructed residual, based on the first reconstructed residual and the second reconstructed residual. The method further includes updating the second reconstructed residual, based on the quantization index and the first reconstructed residual.

An encoding method comprises obtaining (101) an input set of volumetric image data, selecting (103) data from the image data for multiple views based on a visibility of the data from a respective viewpoint at a respective viewing direction and/or within a respective field of view such that a plurality of the views comprises only a part of the image data, encoding (105) each of the views as a separate output set (31), and generating (107) metadata which indicates the viewpoints. A decoding method comprises determining (121) a desired user viewpoint, obtaining (123) the metadata, selecting (125) one or more of the available viewpoints based on the desired user viewpoint, obtaining (127) one or more sets of image data in which one or more available views corresponding to the selected one or more available viewpoints have been encoded, and decoding (129) at least one of the one or more available views.

A three-dimensional data encoding method includes: encoding divided data items to generate encoded data items each corresponding to a respective one of the divided data items, the divided data items being included in subspaces obtained by dividing a current space including three-dimensional points, the divided data items each including one or more three-dimensional points among the three-dimensional points; and generating a bitstream including the encoded data items and control information items each corresponding to a respective one of the encoded data items. Each of the control information items includes a first identifier and a second identifier. The first identifier indicates a subspace corresponding to an encoded data item corresponding to the control information item, and the second identifier indicates a divided data item corresponding to the encoded data item corresponding to the control information item.

A computer-implemented method for designing a 3D modeled object via user-interaction. The method includes obtaining the 3D modeled object and a machine-learnt decoder. The machine-learnt decoder is a differentiable function taking values in a latent space and outputting values in a 3D modeled object space. The method further includes defining a deformation constraint for a part of the 3D modeled object. The method further comprises determining an optimal vector. The optimal vector minimizes an energy. The energy explores latent vectors. The energy comprises a term which penalizes, for each explored latent vector, non-respect of the deformation constraint by the result of applying the decoder to the explored latent vector. The method further includes applying the decoder to the optimal latent vector. This constitutes an improved method for designing a 3D modeled object via user-interaction.

The invention relates to a method of encoding a digital image in order to compress same, the digital image being defined as a point cloud associating a set of N pixels, designated as vertices, to a scalar intensity value. The method aims at establishing triangulation vertices of the digital image and implements the principles of algorithmic topology.

Disclosed is a decoding method for decoding encoded data obtained by encoding a cube that includes three-dimensional point cloud data. The decoding method includes a step of obtaining point occupation states of solids obtained after division in one direction, from the encoded data, wherein the direction of the division is determined based on correlation among the point occupation states of the solids after the division. Another decoding method and a decoding apparatus are also disclosed.

Disclosed herein is a method for encoding an immersive image. The method includes detecting a non-diffuse surface in a first texture image of a first view, generating an additional texture image from the first texture image based on the detected non-diffuse surface, performing pruning on the additional texture image based on a second texture image of a second view, generating a texture atlas based on the pruned additional texture image, and encoding the texture atlas.

A three-dimensional data encoding method includes encoding information of a current node included in an N-ary tree structure of three-dimensional points included in three-dimensional data, where N is an integer greater than or equal to 2. In the encoding, first information is encoded, the first information indicating a range for one or more referable neighboring nodes among neighboring nodes spatially neighboring the current node, and the current node is encoded with reference to a neighboring node within the range.