Point Cloud Encoding Using Sensing Path and Coarse Points
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Solution Overview
Problem
Existing point cloud codecs fail to combine encoding and decoding simplicity, low latency, and high compression performance effectively, particularly for sparse geometry data sensed by spinning Lidar sensors or other flexible sensor configurations, which is crucial for applications like autonomous driving and virtual reality.
Innovation Solution
A method and apparatus for encoding and decoding point clouds using a sensing path defined in a two-dimensional angular coordinate space, where the sensing path is encoded and decoded to represent the sensing trajectory of sensors, allowing for efficient compression and low latency by shifting sensing points to coarse points based on sensor indices, enabling better compression gains and reduced complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing point cloud codecs are used to compress point cloud data, then compression is achieved, but encoding and decoding complexity remains high and latency is not low enough for real-time applications
Solution Approach 1:
The patent segments the point cloud data processing by introducing coarse points that represent groups of fine points. Each coarse point acts as a representative for multiple fine points, dividing the compression task into two levels: coarse point compression and fine point residual compression. This segmentation reduces the overall number of points that need full processing, thereby lowering encoding and decoding complexity while maintaining compression effectiveness.
Solution Approach 2:
The patent introduces coarse points as intermediary elements between the original fine point cloud data and the compressed representation. These coarse points serve as mediators that capture the essential geometry at a lower resolution, allowing the system to compress data more efficiently. The fine points are then compressed relative to their corresponding coarse points, reducing the bitrate required while maintaining accuracy.
2Productivity
If existing point cloud codecs are used to compress point cloud data, then compression is achieved, but latency is high and not suitable for real-time applications like autonomous driving
Solution Approach 1:
By segmenting the point cloud into coarse and fine points, the patent enables parallel processing of different point sets. Coarse points can be processed independently and quickly, providing immediate compression results. Fine points are then processed relative to the already-compressed coarse points, allowing for efficient pipeline processing that reduces overall latency for real-time applications.
3Measurement precision
If fine points are compressed directly without using coarse points, then higher precision is maintained, but bitrate requirement increases significantly
Solution Approach 1:
The patent changes the representation parameters by introducing a hierarchical structure where coarse points provide a low-resolution base and fine points provide high-resolution details. This parameter change allows the system to allocate bitrate efficiently: most bitrate is spent on compressing the coarse points that represent the overall structure, while much less bitrate is needed for the fine points that only need to represent deviations from the coarse structure. This dramatically reduces total bitrate while preserving precision.
Data Source
AI summary
The method of encoding/decoding a point cloud sensed by any type of sensor following a sensing path obtains coarse representations of sensed points and encodes the sensing path and the coarse representations. The sensing path and coarse representations of points are decoded, and points of the point cloud are reconstructed from the decoded sensing path and the decoded coarse representations. The coarse representations of sensed points of the point cloud are coarse points defined in a two-dimensional angular coordinate space, and a coarse point is obtained by shifting a sensing point in the two-dimensional angular coordinate space with shifting values that depend on the sensor index associated with the sensor that sensed the point P of the point cloud.