Sparse Image Interpolation for Vehicle Surroundings Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for converting images between different resolutions in automotive applications, such as digital cameras, are inefficient and lack robustness in determining accurate maps of a vehicle's surroundings, particularly in converting sparse data structures and aligning distance-related data with camera data.
Innovation Solution
A computer-implemented method for interpolation that determines an image-like output data structure by iteratively applying processing operations like average pooling, max pooling, or convolution on sparse input data structures, ensuring that reference data points significantly influence the output, and integrates this method into a neural network-based camera-lidar fusion system for accurate map determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image conversion methods are used to convert images between different resolutions, then the processing can be performed with simple algorithms, but the accuracy and robustness of determining maps of vehicle surroundings deteriorates
Solution Approach 1:
The patent applies preliminary action by performing a pre-determined processing operation (such as pooling or convolution) on the input data structure before the interpolation process. This preprocessing step prepares the data in advance to enhance the influence of reference data points, thereby improving measurement precision without adding significant complexity during the main interpolation phase.
Solution Approach 2:
The patent introduces an intermediary temporary data structure that facilitates the transition from input data to output data. This temporary structure holds intermediate results and allows reference data points to be replaced and propagated through multiple iterations, improving accuracy while keeping each individual processing step relatively simple.
2Productivity
If simple interpolation methods are used to convert sparse data structures, then the processing operation remains fast and simple, but the accuracy of aligning distance-related data with camera data deteriorates
Solution Approach 1:
The patent implements periodic action by repeatedly performing the interpolation process over multiple iterations. In each iteration, the temporary data structure is processed and reference data points are replaced, gradually improving alignment accuracy. This iterative approach maintains reasonable processing speed while significantly enhancing data alignment accuracy compared to single-pass methods.
Solution Approach 2:
The pre-determined processing operation is applied beforehand to enhance reference data points, ensuring that the subsequent interpolation iterations work with pre-enhanced data. This preliminary enhancement of reference data ensures better alignment accuracy without requiring excessive computational resources during the iterative process.
3Manufacturing precision
If reference data points have strong influence on interpolation output, then the accuracy of determined pixels improves, but the complexity of ensuring proper data point replacement increases
Solution Approach 1:
The patent uses a temporary data structure as an intermediary to manage the replacement of reference data points. This temporary structure simplifies the manipulation process by providing a dedicated workspace where reference data points can be systematically replaced and propagated, reducing the complexity of data structure manipulation while ensuring high precision in interpolated pixels.
Solution Approach 2:
The patent segments the interpolation process into distinct steps: determining the temporary data structure, replacing reference data points, and generating the output data structure. This segmentation of the process into manageable stages reduces the overall complexity of data structure manipulation while maintaining high precision through systematic processing of reference data points.
Data Source
AI summary
A computer-implemented method for interpolation comprises the following steps carried out by computer hardware components: determining an image-like input data structure, the image-like input data structure comprising a plurality of data points; determining a plurality of reference data points as a subset of the plurality of data points of the image-like input data structure; determining an image-like temporary data structure based on the image-like input data structure and a pre-determined processing operation; replacing data points of the image-like temporary data structure corresponding to the plurality of reference data points by the plurality of reference data points to obtain an image-like updated temporary data structure; and determining an image-like output data structure based on the image-like updated temporary data structure.