Signal-Based Point Cloud Waveform Encoding
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Solution Overview
Problem
Point clouds are large in size and slow to render due to the extensive data processing required, especially when compared to mesh-based or polygon-based representations, leading to buffering and lag during transmission and rendering.
Innovation Solution
The system generates a signal-based representation of point clouds using an encoder and decoder that convert positional and non-positional elements into waveforms, allowing for compression and transmission over a data network, with intelligent encoding and predictive streaming to reduce latency and bandwidth usage, and a tree-based representation for efficient rendering on remote devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If point cloud data is transmitted in high resolution, then visualization quality is improved, but data transmission time and bandwidth increase
Solution Approach 1:
The point cloud data is divided into multiple octrees, where each octree represents a specific spatial region. This segmentation allows selective transmission of only those octrees that fall within the viewer's current field of view, rather than transmitting all high-resolution data points. The encoder processes and transmits octree structures hierarchically, enabling efficient bandwidth utilization while maintaining visualization quality for the visible region.
Solution Approach 2:
The patent transforms the traditional point cloud representation into a waveform signal domain. By converting spatial coordinates and attributes into frequency domain waveforms, the system enables compact representation and efficient compression. This dimensional transformation from spatial points to temporal waveforms allows real-time streaming with reduced bandwidth requirements while preserving the ability to reconstruct high-quality visualizations.
2Measurement precision
If point cloud data is transmitted in high resolution, then visualization quality is improved, but bandwidth usage increases
Solution Approach 1:
The point cloud is segmented into octree structures that hierarchically organize data points by spatial region. Only octrees corresponding to the visible field of view are encoded and transmitted, significantly reducing the quantity of data that needs to be transmitted over the network. This selective segmentation maintains visualization quality for visible regions while minimizing bandwidth consumption.
Solution Approach 2:
The system dynamically adjusts encoding parameters based on the viewer's position and field of view. By changing which octrees are encoded and at what resolution level, the system optimizes the balance between visualization quality and bandwidth usage. Less critical regions may be encoded at lower resolutions or omitted entirely, while regions within the current view receive higher priority encoding.
3Measurement precision
If point cloud data is rendered with high detail, then visualization quality is improved, but rendering speed decreases
Solution Approach 1:
The decoder reconstructs only the octrees that correspond to the current field of view, rather than processing all high-detail data points in the entire point cloud. This selective reconstruction based on spatial segmentation dramatically reduces the number of data points that need to be processed and rendered, improving rendering speed while maintaining visualization quality for the visible region.
Solution Approach 2:
The encoder performs preprocessing to organize data into octree structures and generate waveform representations in advance. This preliminary organization of data into hierarchical spatial regions allows the decoder to efficiently retrieve and reconstruct only the necessary portions without performing complex processing during real-time rendering, thus improving rendering speed.
4Loss of time
If streaming is implemented in real-time, then latency is reduced, but buffering and lag occur due to data size
Solution Approach 1:
By segmenting the point cloud into hierarchical octree structures, the system enables progressive transmission where coarse-resolution octrees are sent first, followed by finer-detail octrees. This allows the decoder to begin reconstructing and displaying the scene with limited data immediately, reducing initial latency and buffering requirements while progressively improving quality as more data arrives.
Solution Approach 2:
The system implements continuous encoding and transmission of octree waveforms in periodic intervals based on viewer movement and field of view changes. This periodic streaming approach, combined with predictive pre-fetching of adjacent regions, maintains real-time performance by continuously updating the visualization with fresh data while managing buffer occupancy through controlled transmission rates.
Data Source
AI summary
Disclosed is an encoding and decoding system and associated methods for producing a compressed waveform that encodes data points of a point cloud in a format and size that may be transmitted over a data network, decompressed, decoded, and rendered on a remote device without the buffering or lag associated with transmitting and rendering an uncompressed point cloud. The encoder receives a request from a remote device to access the point cloud, encodes a set of data points from the point cloud as one or more signals derived from values defined for the positional and non-positional elements of each data point from the set of data points, generates one or more compressed waveforms from compressing the one or more signals and transmits the one or more compressed waveforms to the remote device in response to the request for decompression, decoding, and image rendering.