Lidar Point Cloud Coding via Video Signal Conversion
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Solution Overview
Problem
Conventional lidar point cloud coding methods are inefficient due to the unique characteristics of lidar sensors, which generate multiple points for a given time interval, requiring improved encoding and decoding techniques to leverage these features effectively.
Innovation Solution
A method and apparatus that convert lidar point clouds into video signals using features of lidar sensors, employing a coordinate system conversion, video generation, preprocessing, and encoding/decoding to enhance coding efficiency, utilizing techniques like H.264/AVC, H.265/HEVC, and other video coding methods to encode and decode the converted video signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional point cloud coding methods are used for lidar data, then the coding process is simple, but the coding efficiency is low due to the unique characteristics of lidar sensors generating multiple points per time interval
Solution Approach 1:
The patent replaces conventional point cloud coding methods with video coding methods (H.264/AVC, H.265/HEVC). By converting lidar point cloud data into video signal format and applying established video compression algorithms, the system achieves significantly improved coding efficiency while leveraging mature video coding infrastructure rather than developing new point cloud-specific codecs.
Solution Approach 2:
The patent transforms the data representation parameters by converting point cloud coordinates into video-compatible signal formats. This involves changing how spatial and temporal information is encoded, mapping lidar measurements onto video frame structures, and adjusting parameter representations to match video coding standards, thereby enabling efficient compression of lidar's unique multi-point temporal data.
2Measurement precision
If lidar sensors generate multiple points for a given time interval, then more detailed spatial information is captured, but the data processing complexity increases
Solution Approach 1:
The patent segments the lidar point cloud data into discrete temporal intervals or frames, similar to video frame structures. By dividing the continuous stream of multiple points per time interval into organized segments that correspond to video frames, the system maintains detailed spatial information while making the data manageable and compatible with sequential video processing algorithms.
Solution Approach 2:
The patent introduces a temporal dimension organization by mapping multiple spatial points captured at different time intervals onto a video-like temporal sequence. This dimensional transformation organizes the multi-point data structure into a format where spatial details are preserved within frames while temporal progression follows video standards, simplifying subsequent processing.
Data Source
AI summary
A lidar point cloud coding method and apparatus convert a point cloud to video signals by using features of a lidar sensor to improve encoding efficiency of lidar point cloud coding. The lidar point cloud coding method and the apparatus encode/decode the converted video signals.