3D Motion Flow Estimation Using Energy Functions
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Solution Overview
Problem
Current motion flow estimation systems for autonomous vehicles lack accuracy and efficiency in determining three-dimensional motion flow, particularly in dynamic environments, which can impact safety and operational efficiency.
Innovation Solution
A computer-implemented method using machine-learned models to estimate three-dimensional motion flow by accessing scene data from multiple time intervals, generating extracted visual cues with machine-learned feature extraction models, encoding these cues using energy functions, and determining motion estimates with machine-learned inference models, while adjusting parameters based on loss comparisons to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional motion flow estimation methods are used, then computational resources are consumed, but accuracy and efficiency of three-dimensional motion flow estimation deteriorate
Solution Approach 1:
The system segments the motion estimation problem into multiple independent components: optical flow estimation for two-dimensional motion, stereo vision for depth calculation, and structure from motion for three-dimensional reconstruction. Each component is processed separately using specialized algorithms, improving both accuracy and computational efficiency compared to monolithic traditional methods.
Solution Approach 2:
The invention transitions from traditional two-dimensional optical flow estimation to three-dimensional motion flow estimation by incorporating depth information through stereo vision and structure from motion techniques. This dimensional expansion enables accurate estimation of three-dimensional object motion, position, and velocity that was not achievable with conventional two-dimensional methods.
2Measurement precision
If more computational resources are allocated to motion flow estimation, then accuracy improves, but computational efficiency and resource utilization worsen
Solution Approach 1:
The system performs preliminary actions by pre-processing input images to extract features and pre-calculating depth maps using stereo vision before the main motion estimation process. This preliminary processing reduces the computational burden during real-time execution, improving efficiency while maintaining accuracy through the use of pre-computed information.
Solution Approach 2:
The invention replaces traditional mechanical computation methods with machine learning-based approaches. Neural networks are trained offline to perform motion estimation tasks, replacing real-time iterative optimization algorithms. This substitution shifts computational load from runtime processing to training phase, significantly improving real-time efficiency while maintaining high accuracy.
3Productivity
If simple estimation algorithms are used, then computational efficiency is maintained, but accuracy in dynamic environments deteriorates
Solution Approach 1:
The system dynamically changes parameters based on environmental conditions. In dynamic environments with moving objects, the algorithm adjusts optical flow parameters, stereo matching parameters, and structure from motion parameters to optimize for motion detection accuracy. This adaptive parameter adjustment maintains high accuracy in varying conditions while preserving computational efficiency through selective parameter modification rather than complete algorithm reconfiguration.
Data Source
AI summary
Systems, methods, tangible non-transitory computer-readable media, and devices associated with motion flow estimation are provided. For example, scene data including representations of an environment over a first set of time intervals can be accessed. Extracted visual cues can be generated based on the representations and machine-learned feature extraction models. At least one of the machine-learned feature extraction models can be configured to generate a portion of the extracted visual cues based on a first set of the representations of the environment from a first perspective and a second set of the representations of the environment from a second perspective. The extracted visual cues can be encoded using energy functions. Three-dimensional motion estimates of object instances at time intervals subsequent to the first set of time intervals can be determined based on the energy functions and machine-learned inference models.