Single Camera Depth Map Generation via Image Segmentation
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Solution Overview
Problem
Current 3D depth mapping devices are limited to single applications and require multiple cameras or camera arrays, which increase device volume and complexity, and struggle with determining depth from a single image effectively, especially when objects move quickly.
Innovation Solution
A single camera module with fixed near-field focus captures images, segments them into regions, determines focus metrics, and generates depth maps by mapping these metrics to depth values, allowing for object tracking and gesture recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple cameras or camera arrays are used to determine depth, then measurement precision is improved, but device complexity and volume increase
Solution Approach 1:
The patent segments the image into multiple regions of interest and calculates focus metrics for each region independently. This allows depth information to be extracted from a single camera by analyzing different spatial segments, achieving depth measurement precision comparable to multi-camera systems without the hardware complexity
Solution Approach 2:
The patent changes the parameter being measured from multiple camera positions to focus metric values within a single camera image. By using focus metrics (such as variance, gradient, or Laplacian) as a proxy for depth, the system achieves depth measurement capability from a single sensor, resolving the contradiction between measurement precision and device complexity
2Manufacturing precision
If multiple cameras or camera arrays are used to determine depth, then depth mapping accuracy is improved, but device volume increases
Solution Approach 1:
The image is divided into multiple regions of interest, and depth is calculated for each region using focus metrics. This segmentation approach enables accurate depth mapping across the entire scene using only a single camera, avoiding the need for multiple cameras that would increase device volume
Solution Approach 2:
The patent creates a depth map (a copy of spatial information) from focus metric data obtained from a single camera image. This computational copying of depth information replaces the need for physical multiple-camera copies, maintaining depth map accuracy while minimizing device volume
3Productivity
If focus metrics are calculated for multiple regions from a single image, then depth map generation speed is improved, but processing complexity increases
Solution Approach 1:
The patent segments the image into regions of interest and calculates focus metrics for each segment in parallel. This segmentation enables efficient depth map generation by distributing the computational workload across multiple regions, improving processing throughput while managing complexity through modular region-based analysis
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables efficient generation of depth maps from a single image, reducing hardware and processing requirements, and effectively tracks objects and recognizes gestures for various applications, such as automotive environments.
Implementation Method 1
a single camera module with a fixed near field focus configured to capture a single image
Implementation Method 2
an image divider configured to segment the image into a plurality of regions
Implementation Method 3
a focus metric determiner configured to determine a focus metric for each of the plurality of regions
Implementation Method 4
a depth map generator configured to map the focus metric into a depth value for each of the plurality of regions and combine the plurality of depth values to generate a depth map
Data Source
AI summary
A camera module with a fixed near field focus is configured to capture a single image. That single image is segmented by an image divider a number of regions. A focus metric determiner then determines a focus metric for each of the regions. A depth map generator maps the focus metric into a depth value for each of the regions and combines the depth values to generate a depth map.