18354443. USING VERTICAL PREDICTION FOR GEOMETRY POINT CLOUD COMPRESSION simplified abstract (QUALCOMM Incorporated)

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USING VERTICAL PREDICTION FOR GEOMETRY POINT CLOUD COMPRESSION

Organization Name

QUALCOMM Incorporated

Inventor(s)

Adarsh Krishnan Ramasubramonian of Irvine CA (US)

Geert Van Der Auwera of San Diego CA (US)

Marta Karczewicz of San Diego CA (US)

USING VERTICAL PREDICTION FOR GEOMETRY POINT CLOUD COMPRESSION - A simplified explanation of the abstract

This abstract first appeared for US patent application 18354443 titled 'USING VERTICAL PREDICTION FOR GEOMETRY POINT CLOUD COMPRESSION

Simplified Explanation

The patent application describes a point cloud encoder and decoder that use predictive geometry coding and a vertical predictor to code point cloud data. The vertical predictor is a previously coded point in the point cloud with a different laser ID than the currently coded point. The encoder and decoder can determine a pivot laser ID, determine a vertical predictor for a current point, and code the current point using the vertical predictor and predictive geometry decoding.

  • The point cloud encoder and decoder use predictive geometry coding and a vertical predictor.
  • A vertical predictor is a previously coded point with a different laser ID.
  • The encoder and decoder determine a pivot laser ID.
  • The encoder and decoder determine a vertical predictor for a current point based on a second point with a different laser ID.
  • The current point is coded using the vertical predictor and predictive geometry decoding.

Potential applications of this technology:

  • Autonomous vehicles: Point cloud data is crucial for autonomous vehicles to perceive their surroundings accurately. This technology can improve the compression and transmission of point cloud data, enabling more efficient processing and communication between autonomous vehicles and their control systems.
  • Virtual reality and augmented reality: Point cloud data is used to create realistic virtual and augmented reality environments. This technology can enhance the encoding and decoding of point cloud data, leading to more immersive and high-quality virtual and augmented reality experiences.
  • Robotics: Robots often rely on point cloud data for object recognition and navigation. This technology can optimize the encoding and decoding of point cloud data, enabling robots to process and interpret their environment more efficiently.

Problems solved by this technology:

  • Efficient compression: Point cloud data can be large and require significant storage and bandwidth. This technology improves the compression of point cloud data, reducing storage requirements and enabling faster transmission.
  • Accurate reconstruction: Point cloud data is used to reconstruct 3D environments. This technology enhances the encoding and decoding process, ensuring accurate reconstruction of the original point cloud data.
  • Real-time processing: Point cloud data is often processed in real-time applications such as autonomous vehicles and robotics. This technology improves the efficiency of encoding and decoding, enabling faster real-time processing of point cloud data.

Benefits of this technology:

  • Improved efficiency: The encoding and decoding process is optimized, leading to faster processing and transmission of point cloud data.
  • Enhanced compression: Point cloud data can be compressed more effectively, reducing storage requirements and enabling faster transmission.
  • Accurate reconstruction: The encoding and decoding process ensures accurate reconstruction of the original point cloud data, preserving its integrity and quality.


Original Abstract Submitted

A point cloud encoder and decoder are configured to code point cloud data using predictive geometry coding and a vertical predictor. A vertical predictor is a previously coded point in the point cloud having a different laser ID compared to the currently coded point. The point cloud encoder and decoder may be configured to determine a pivot laser ID, determine a vertical predictor for a current point of the point cloud data, wherein the vertical predictor is based on a second point having a second laser ID different than the pivot laser ID, and code the current point using the vertical predictor and predictive geometry decoding.

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