Rolling Shutter Camera 3D Reconstruction Using Vehicle Motion Parameters
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Solution Overview
Problem
Current 3D reconstruction methods using rolling shutter cameras in applications like driver assistance and robotics suffer from systematic errors due to the time-offset exposure and readout of pixels, which are not adequately addressed by existing methods, especially in embedded systems with limited resources.
Innovation Solution
A method that compensates for the time-offset distortion in rolling shutter cameras by using vehicle and driving parameters to process image data, allowing for precise 3D reconstruction without prior knowledge of scene geometry, and enables efficient implementation in embedded systems by modeling the camera's egomotion using linear differential equations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If rolling shutter cameras are used for 3D reconstruction, then cost is reduced and device complexity is lowered, but systematic 3D reconstruction errors occur due to time-offset exposure and readout
Solution Approach 1:
The patent transforms the fixed rolling shutter readout process into a parameterized model where each pixel's exposure time and readout time are explicitly defined. By introducing time parameters (t_exposure, t_readout) and camera motion parameters (position p(t), orientation q(t)) into the projection model, the system can mathematically compensate for rolling shutter effects without changing the physical camera hardware, thus maintaining cost benefits while improving 3D reconstruction precision.
Solution Approach 2:
The patent introduces an intermediate computational model that acts as a mediator between the rolling shutter camera and the 3D reconstruction algorithm. This model includes a rolling shutter projection function that accounts for time-offset effects, serving as a bridge that translates rolling shutter image data into a format suitable for accurate 3D reconstruction without requiring global shutter hardware.
2Manufacturing precision
If complex computation methods like bundle adjustment are used to compensate for rolling shutter effects, then 3D reconstruction precision is improved, but implementation in embedded systems becomes inefficient due to high computational requirements
Solution Approach 1:
The patent segments the 3D reconstruction process into distinct computational stages: (1) camera egomotion estimation from image sequences, (2) rolling shutter parameter identification, and (3) 3D point reconstruction using the segmented model. This segmentation allows each stage to be optimized independently, with the rolling shutter compensation integrated efficiently into the existing SfM pipeline rather than requiring computationally intensive global optimization methods.
Solution Approach 2:
The patent employs dynamic modeling of camera motion during the rolling shutter exposure period, using time-varying position p(t) and orientation q(t) functions. This dynamic approach allows the system to adapt to different camera motion patterns (constant velocity, acceleration, rotation) without requiring a fixed computational algorithm, improving both precision and efficiency across various driving scenarios.
Data Source
AI summary
A method for ascertaining an image of the surroundings of a vehicle. The method includes reading in first image data and at least second image data, the first image data representing image data of a first image recording area of a camera in or on a vehicle and the second image data representing image data from a second image recording area of the camera differing from the first image recording area, and the second image data having been recorded chronologically after the first image data. The method further includes processing the second image data using a vehicle parameter and/or driving parameter, to obtain processed second image data. Finally, the method includes combining the first image data with the processed second image data to obtain the image of the surroundings of the vehicle.