Camera-Invariant 3D Vehicle Depth Estimation Across Camera Setups
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Solution Overview
Problem
Existing methods for estimating 3D properties of objects in computer vision for autonomous driving are challenged by the diversity of imaging sensor equipment, leading to inaccurate and limited applicability of models due to reliance on specific camera configurations and mounting positions, necessitating multiple model training and deployment.
Innovation Solution
Training predictive models to estimate camera-invariant 3D properties, allowing a single model to be deployed across various camera systems by predicting values independent of camera parameters, and using invertible transformations to recover absolute 3D properties at inference time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If models are trained for specific camera configurations and mounting positions, then prediction accuracy for those specific configurations is improved, but the number of models required increases and applicability to other configurations deteriorates
Solution Approach 1:
The patent transforms the prediction target from absolute 3D properties (which are camera-parameter-dependent) to camera-invariant 3D properties through invertible transformations. This parameter change in the prediction space allows a single model to make accurate predictions across diverse camera configurations without retraining, resolving the contradiction between maintaining high accuracy and achieving broad adaptability.
2Measurement precision
If multiple models are trained for different camera configurations, then prediction accuracy for each configuration is maintained, but device complexity and training time increase
Solution Approach 1:
The patent creates a universal predictive model that can handle multiple camera configurations through camera-invariant property formulation. This single model performs the function of what would otherwise require multiple configuration-specific models, reducing device complexity while maintaining prediction accuracy across all configurations.
3Measurement precision
If camera-specific models are deployed, then measurement precision for that camera is improved, but the time and resources required for model training and deployment increase
Solution Approach 1:
By changing the parameter space from camera-dependent absolute 3D properties to camera-invariant properties, the patent enables a single model training process to serve all camera configurations. This eliminates the need for repeated training for each camera, significantly reducing time loss while maintaining estimation accuracy.
Data Source
AI summary
Determining an absolute depth estimate of a vehicle object in a 2D image captured by a camera includes receiving the 2D image captured by the camera. It further includes determining, at least in part by using a segmentation model, a plurality of pixels corresponding to the vehicle object in the 2D image. It furhter includes using a prediction model to determine, for each pixel in the plurality of pixels determined at least in part by using the segmentation model, a correspondng camera invariant distance value comprising a predicted height of a cross-section of the vehicle object that is visible within a given pixel. It further includes determining the absolute depth estimate of the vehicle object in the 2D image based on: a focal length of the camera that captured the 2D image; and an aggregation of the predicted heights determined for each of the pixels in the plurality of pixels.