Track-Based Depth Image Structure for Sparse Point Cloud Overlap
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Solution Overview
Problem
Existing depth images generated from sparse point cloud data suffer from overlapping depth information, leading to loss of accuracy and increased computational complexity, power consumption, and latency, which is critical in real-time applications like autonomous driving.
Innovation Solution
A depth image data structure is introduced, where depth information is organized into tracks representing intervals along the image's first dimension, with tuples storing both depth values and y-coordinates, sorted to facilitate efficient traversal and reduce unnecessary memory reads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If depth information is stored in a conventional image structure, then the data can be processed using standard image processing algorithms, but overlapping depth information occurs leading to loss of accuracy
Solution Approach 1:
The depth image data structure segments depth information by organizing it into multiple tracks, where each track represents a specific depth range or interval. This segmentation prevents overlapping depth information by assigning each depth value to a dedicated track, thereby preserving measurement precision without information loss.
2Reliability
If all pixels in the depth image are processed, then complete scene coverage is achieved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent extracts and processes only the necessary depth information by utilizing the track structure to identify and process only relevant depth ranges. This extraction approach maintains scene coverage reliability while reducing computational complexity by avoiding unnecessary processing of all pixels.
Solution Approach 2:
The patent applies partial action by processing only the depth information within specific tracks that are relevant to the current processing task, rather than processing all depth information excessively. This selective processing maintains reliability for critical areas while reducing overall computational complexity.
3Loss of information
If depth information for all pixels is stored, then complete depth data is available, but memory usage and processing latency increase
Solution Approach 1:
The depth image data structure segments depth information into tracks representing different depth intervals. This segmentation allows the system to store complete depth data while reducing processing latency by enabling selective access to only the relevant tracks needed for current processing tasks, rather than processing all depth data.
4Ease of manufacture
If conventional depth image processing is used, then standard algorithms can be applied, but power consumption increases due to unnecessary processing
Solution Approach 1:
The patent extracts only the necessary depth information from the track-based data structure for processing. This extraction maintains compatibility with standard algorithms while reducing power consumption by eliminating unnecessary processing of irrelevant depth data, focusing computational resources only on essential information.
Data Source
AI summary
The present disclosure provide techniques for processing image depth information. A method may include obtaining a depth image data structure representative of depth information for pixels in an image corresponding to coordinates in the image, wherein the depth image data structure comprises depth values associated with a subset of the pixels, and comprises: a plurality of tracks, each track representing a respective interval along a first dimension of the image, wherein: the subset of pixels are located at coordinates of the image represented by first track(s); and each first track includes respective depth information (e.g., a respective depth value and portion of a respective coordinate) for each respective pixel of respective one or more pixels of the subset of pixels located at respective one or more coordinates of the image represented by the track; and processing the depth image data structure.