# US 11,445,161 B2 · System and method for streaming visible portions of volumetric video > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,445,161 B2 - Document ID: US11445161B2 - Status: Granted - Kind: B2 - Application number: 17/197,276 - Filed: 2021-03-10 - Published: 2021-06-24 - Granted: 2022-09-13 - Earliest claimed: 2019-08-14 - Assignees: AT&T Intellectual Property I, L.P. - Applicants: AT&T Intellectual Property I, L.P. - Inventors: Bo Han; Jackson Jarrell Pair; Cheuk Yiu Ip - Web page: https://patentgrep.com/patent/US11445161B2 ## CPC - H04N 13/194: Transmission of image signals - H04N 13/282: for generating image signals corresponding to three or more geometrical viewpoints, e.g. multi-view systems - G06N 3/044: Recurrent networks, e.g. Hopfield networks - G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] - G06N 3/08: Learning methods - G06N 20/00: Machine learning - G06N 20/10: using kernel methods, e.g. support vector machines [SVM] - G06T 7/174: involving the use of two or more images - H04N 13/117: the virtual viewpoint locations being selected by the viewers or determined by viewer tracking - G06N 20/20: Ensemble learning - H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract Aspects of the subject disclosure may include, for example, a device having a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising predicting a viewpoint within a volumetric video based on a movement input provided by a viewer, resulting in a predicted viewpoint at a future time, retrieving a cell occupancy bitmap for a point cloud of the volumetric video for the future time, determining visible cells based on the cell occupancy bitmap and the predicted viewpoint, where the visible cells are not obscured by points in other cells, and retrieving points of the point cloud that are within the visible cells prior to the future time. ## Claims ### Claim 1 A device, comprising: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising: predicting a viewpoint within a volumetric video based on a movement input provided by a viewer, resulting in a predicted viewpoint at a future time; retrieving a cell occupancy bitmap for a point cloud of the volumetric video for the future time, wherein the point cloud is divided into a plurality of three-dimensional cells such that, for the predicted viewpoint, particular points in a first cell of the plurality of three-dimensional cells obscure a second cell of the plurality of three-dimensional cells; determining visible cells of the plurality of three-dimensional cells based on the cell occupancy bitmap and the predicted viewpoint, wherein the visible cells exclude the second cell; and retrieving points of the point cloud that are within the visible cells prior to the future time. ### Claim 2 (depends on 1) The device of claim 1, wherein the movement input comprises six degrees of freedom, and wherein the processing system comprises a plurality of processors operating in a distributed computing environment. ### Claim 3 (depends on 1) The device of claim 1, wherein the operations further comprise: selecting a machine learning algorithm to predict the viewpoint; and predicting the viewpoint using the machine learning algorithm. ### Claim 4 (depends on 3) The device of claim 3, wherein the machine learning algorithm is a linear regression, a ridge regression, or a combination thereof. ### Claim 5 (depends on 3) The device of claim 3, wherein the machine learning algorithm is a deep learning scheme. ### Claim 6 (depends on 5) The device of claim 5, wherein the deep learning scheme is a Long Short Term Memory. ### Claim 7 (depends on 1) The device of claim 1, wherein the operations further comprise decompressing the cell occupancy bitmap. ### Claim 8 (depends on 1) The device of claim 1, wherein the operations further comprise retrieving additional cells for the predicted viewpoint. ### Claim 9 (depends on 8) The device of claim 8, wherein the operations further comprise: merging the additional cells with the visible cells, resulting in merged cells; and rendering points of the point cloud that are within the merged cells. ### Claim 10 (depends on 9) The device of claim 9, wherein the operations further comprise retrieving a base layer for the additional cells. ### Claim 11 (depends on 10) The device of claim 10, wherein the operations further comprise retrieving enhancement layers for the additional cells responsive to available network bandwidth being sufficient. ### Claim 12 A non-transitory, machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, the operations comprising: segmenting a point cloud of a volumetric video into three-dimensional cells; generating a cell occupancy bitmap for a frame of the volumetric video to be used at a future time; transmitting the cell occupancy bitmap for the frame, responsive to a first request received from a video player; and responsive to a second request received from the video player, transmitting cells to the video player for use at the future time, wherein the second request is based on at least some of the cells being visible to a viewer of the video player at a predicted viewpoint. ### Claim 13 (depends on 12) The non-transitory, machine-readable medium of claim 12, wherein the processing system comprises a plurality of processors operating in a distributed computing environment. ### Claim 14 (depends on 12) The non-transitory, machine-readable medium of claim 12, wherein the operations further comprise compressing the cell occupancy bitmap. ### Claim 15 (depends on 12) The non-transitory, machine-readable medium of claim 12, wherein the operations further comprise transmitting additional cells for the predicted viewpoint. ### Claim 16 (depends on 15) The non-transitory, machine-readable medium of claim 15, wherein the operations further comprise transmitting a base layer for the additional cells. ### Claim 17 (depends on 16) The non-transitory, machine-readable medium of claim 16, wherein the operations further comprise transmitting enhancement layers for the additional cells responsive to available network bandwidth being sufficient. ### Claim 18 A method, comprising: predicting, by a processing system including a processor, a viewpoint within a volumetric video based on movement input by a viewer, resulting in a predicted viewpoint at a future time, wherein a point cloud of the volumetric video is segmented into three-dimensional cells; retrieving, by the processing system, a cell occupancy bitmap of the point cloud of the volumetric video for the future time; determining, by the processing system, visible cells based on the cell occupancy bitmap and the predicted viewpoint; and fetching, by the processing system, the visible cells prior to the future time. ### Claim 19 (depends on 18) The method of claim 18, further comprising rendering, by the processing system, points of the point cloud that are within the visible cells at the future time. ### Claim 20 (depends on 18) The method of claim 18, further comprising: retrieving, by the processing system, additional cells for the predicted viewpoint; merging, by the processing system, the additional cells with the visible cells, resulting in merged cells; and rendering, by the processing system, points of the point cloud that are within the merged cells at the future time.