Dynamic Sensor Capture for Complete 3D Scene Models
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Solution Overview
Problem
Street-level images often suffer from obstructions by obstacles like vegetation, vehicles, and pedestrians, leading to incomplete 3D models when used for scene reconstruction, as these obstacles block views and prevent areas behind them from being captured.
Innovation Solution
A system that controls vehicle-mounted image and location sensors to adjust capture rates and timing based on the presence of targets of interest and occluders, using machine learning to classify surfaces and determine optimal capture strategies, ensuring sufficient data collection for complete 3D model generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If street-level images are captured at fixed intervals, then the capture system is simple to operate, but the images are obstructed by obstacles like vegetation, vehicles, and pedestrians leading to incomplete 3D models
Solution Approach 1:
The patent implements dynamic capture rate adjustment based on detected obstacles. The system transitions from fixed-interval capture to variable-rate capture, increasing capture frequency when occluders are detected and decreasing it when the path is clear. This dynamic adaptation resolves the contradiction by making the system flexible enough to overcome obstacles while maintaining operational simplicity through automated control.
Solution Approach 2:
The system uses feedback from obstacle detection to adjust capture timing. Sensors detect occluders in real-time, and this information feeds back to the capture controller, which then modifies the capture rate accordingly. This closed-loop feedback mechanism ensures complete 3D model generation while keeping the control system manageable through rule-based automation.
2Reliability
If the capture rate is increased to overcome obstructions, then more complete 3D models are achieved, but the data collection time and processing requirements increase
Solution Approach 1:
The capture rate is dynamically adjusted based on real-time obstacle detection rather than maintaining a constantly high rate. The system captures images at normal intervals when the path is clear and only increases capture frequency when occluders are detected. This dynamic approach achieves complete 3D models while minimizing unnecessary data collection time.
Solution Approach 2:
The system changes the temporal parameter (capture interval) based on environmental conditions. By monitoring obstacle presence and adjusting the capture time interval accordingly, the system optimizes the balance between model completeness and collection time, capturing sufficient data without excessive delays.
3Reliability
If sensors capture data continuously at high rate, then complete coverage is achieved, but the quantity of data to be processed increases significantly
Solution Approach 1:
The system dynamically adjusts capture frequency to match actual needs. Instead of continuous high-rate capture, it uses variable-rate capture triggered by obstacle detection events. This ensures sufficient data for complete 3D modeling while dramatically reducing the total volume of captured images and associated processing requirements.
Solution Approach 2:
The capture system changes operational parameters (time interval between captures) based on environmental conditions. By extending intervals during clear periods and shortening them during obstacle encounters, the system maintains model completeness while optimizing data volume to minimal necessary levels.
4Productivity
If the capture rate is dynamically adjusted based on obstacles, then data efficiency is improved, but the system complexity increases
Solution Approach 1:
The system implements feedback-based control where obstacle detection results directly influence capture rate decisions. This automated feedback loop improves data collection efficiency by capturing only when necessary, while the rule-based feedback mechanism keeps system complexity manageable compared to more sophisticated adaptive systems.
Solution Approach 2:
The capture system serves itself by automatically adjusting its own operation based on environmental feedback. The obstacle detection subsystem provides information that the capture controller uses to self-regulate its data collection rate, eliminating the need for external manual control and improving efficiency while maintaining reasonable system complexity through modular design.
Data Source
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AI summary
The disclosure provides for a method of controlling one or more sensors on a moving vehicle that is executable by one or more computing devices. The one or more computing devices may detect a first surface at a first location and a second surface at a second location using the one or more sensors. The second surface may be classified as a target of interest. Then the one or more computing devices may determine one or more timing characteristics of the one or more sensors based on a pose or motion of the one or more sensors relative to the first location of the first surface and the second location of the second surface. Then, the one or more computing devices may control the one or more sensors to capture data according to the determined one or more timing characteristics.