2D Image Projection Onto 3D Point Clouds for Sensor Alignment
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Solution Overview
Problem
Current methods for rendering 2D and 3D data within a 3D virtual environment for autonomous vehicles are processing-intensive and time-consuming, making it difficult to align and comprehend 3D LIDAR and 2D image data effectively.
Innovation Solution
A method that generates a 3D virtual environment, defines vehicle origins, and projects 2D images onto image planes within the environment, allowing for concurrent rendering of 3D LIDAR and 2D data, enabling quick alignment and annotation by human annotators or technicians.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods for rendering 2D and 3D data are used, then comprehensive data alignment is achieved, but processing time and computational load increase significantly
Solution Approach 1:
The patent segments the rendering process by creating separate rendering passes for different data types (2D camera images and 3D LIDAR point clouds). Each data type is rendered independently to its respective plane, avoiding the need to process and align them together in a single intensive operation. This segmentation reduces overall processing time while maintaining alignment accuracy through coordinated origin points and transformation matrices.
Solution Approach 2:
The patent projects 2D camera images onto a 2D image plane and 3D LIDAR data onto a 3D display plane within a virtual environment, utilizing different dimensional representations. By maintaining separate dimensional spaces for different sensor data types and coordinating them through transformation matrices and origin points, the system achieves accurate alignment without the computational burden of unified high-dimensional processing.
2Productivity
If 2D and 3D data are rendered concurrently, then annotation efficiency improves, but system complexity increases
Solution Approach 1:
The system segments the rendering workflow into distinct passes for 2D and 3D data, allowing annotators to work with different data types sequentially or independently. This segmentation manages system complexity by breaking down the concurrent rendering task into smaller, more manageable operations while still achieving the productivity benefit of having both data types available for annotation.
Solution Approach 2:
The patent performs preliminary actions by pre-defining origin points, transformation matrices, and projection parameters for both 2D and 3D data before the actual annotation process. This preliminary setup establishes the coordinate systems and alignment relationships in advance, reducing the complexity of real-time concurrent rendering while enabling efficient annotation productivity.
3Reliability
If sensor alignment verification is enhanced, then data quality improves, but processing requirements increase
Solution Approach 1:
The patent replaces complex mechanical alignment verification processes with computational methods using transformation matrices and origin point coordination. By substituting mathematical transformations for physical alignment adjustments, the system achieves reliable sensor alignment verification with reduced processing energy requirements, as the virtual environment naturally coordinates different sensor data types through defined transformation relationships.
Data Source
AI summary
One variation of a method includes: accessing a 2D color image recorded by a 2D color camera and a 3D point cloud recorded by a 3D depth sensor at approximately a first time, the 2D color camera and the 3D depth sensor defining intersecting fields of view and facing outwardly from an autonomous vehicle; detecting a cluster of points in the 3D point cloud representing a continuous surface approximating a plane; isolating a cluster of color pixels in the 2D color image depicting the continuous surface; projecting the cluster of color pixels onto the plane to define a set of synthetic 3D color points in the 3D point cloud, the cluster of points and the set of synthetic 3D color points representing the continuous surface; and rendering points in the 3D point cloud and the set of synthetic 3D color points on a display.