Point Cloud Data Transmission via Viewport-Adaptive Segmentation

The point cloud data is divided into multiple segments or slices corresponding to different spatial regions or depth ranges. Each segment can be independently encoded, transmitted, and decoded. This segmentation reduces the complexity of processing large-scale point cloud data while maintaining overall data quality, as only relevant segments need to be processed for specific viewing scenarios.

Different quality levels are applied to different regions of the point cloud data based on their importance and visibility. High-priority regions (such as foreground objects or user viewport areas) are encoded with higher quality, while less important regions use lower quality encoding. This approach maintains service quality for critical areas while reducing overall data volume.

Point cloud data is pre-processed, pre-segmented, and pre-encoded into multiple layers or resolutions before transmission. This preliminary organization allows the receiver to quickly access and decode only the necessary data portions without waiting for complete data transmission, thereby reducing latency while maintaining processing accuracy for the required regions.

Only the essential or relevant portions of point cloud data are extracted and transmitted based on predicted user needs, viewport information, or application requirements. Non-critical data is either omitted or transmitted at lower fidelity, reducing transmission time while preserving sufficient accuracy for the extracted components.

The encoding and decoding processes are made adaptive and dynamic, adjusting processing complexity based on available resources, time constraints, and quality requirements. Multiple encoding passes or progressive refinement techniques allow the system to start with low-complexity decoding and progressively improve quality as computational resources become available, balancing service quality with processing complexity.

Instead of processing the entire high-resolution point cloud data set, the system processes only the necessary portions at high resolution while using lower resolution for the rest. This partial processing approach maintains service quality for critical regions while significantly reducing overall encoding and decoding complexity.

The point cloud data is encoded with multi-layer or progressive structures that provide universal adaptability across different applications and devices. The same encoded bitstream can serve multiple purposes (e.g., different viewports, resolutions, and quality requirements) without requiring separate transmissions, thereby maintaining adaptability while improving transmission efficiency through shared encoding.

Point cloud data is segmented into multiple independent but complementary components or layers that can be selectively transmitted based on specific application needs. This segmentation enables efficient transmission by allowing receivers to obtain only the segments relevant to their particular use case, improving transmission efficiency while preserving overall adaptability through the modular structure.