Point Cloud Feature Prioritization for Bandwidth-Constrained Streaming
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Solution Overview
Problem
Existing adaptive streaming techniques are inadequate for transferring 3D content in its original form over data networks with variable conditions and resource availability, as they rely on converting 3D data to 2D rendering, which is not always feasible or desired.
Innovation Solution
Adaptive point cloud streaming that selectively streams points representing important features of a 3D object out-of-order, conserving bandwidth by initially transmitting a subset of points with positional data and optionally minimal color information, and subsequently adding detailed color information when resources permit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If 3D content is converted to 2D rendering for adaptive streaming, then adaptive streaming can be implemented, but the original 3D data cannot be transmitted
Solution Approach 1:
The patent segments the point cloud data into multiple priority levels (e.g., foreground objects, background objects, less important regions). This segmentation allows the system to transmit only the most important segments first, enabling adaptive streaming of 3D content while preserving the original data format and allowing progressive reconstruction of the full 3D scene as more data becomes available.
2Measurement precision
If all points of a feature are streamed to ensure complete visualization, then visualization accuracy is improved, but bandwidth consumption increases
Solution Approach 1:
The patent applies partial action by transmitting only a subset of points that are sufficient to convey the essential visual characteristics of each feature. Rather than streaming all points representing a feature, the system identifies and transmits the most visually significant points (e.g., boundary points, high-contrast points, or points defining key geometric features), achieving recognizable visualization with reduced data transmission.
Solution Approach 2:
The patent applies local quality by differentiating the importance of different regions and features within the point cloud. Different areas of the 3D content are assigned different quality levels based on their visual significance. High-priority regions (such as foreground objects or areas of interest) receive more points and higher detail, while lower-priority regions (such as background or less important areas) receive fewer points, optimizing the distribution of bandwidth resources across different spatial locations.
Data Source
AI summary
A streaming system and associated methods provide adaptive streaming of point cloud data for out-of-order presentation of important visual features before less important visual features. The adaptive streaming includes retrieving the points for a requested point cloud, differentiating different sets of points that represent different features in the point cloud, prioritizing each set of points based on a feature that is represented by that set of points, and streaming the different sets of points across a data network to a client device in an order that is determined from the prioritization. Moreover, the adaptive streaming may dynamically select a different subset of points from each set of points and different subset of point data to stream with each selected subset of points to visually convey important detail of each feature without all the corresponding points or data that make up that feature in the point cloud.