Monocular Image Lifting Function for 3D Vehicle Representation
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Solution Overview
Problem
Current systems for autonomous vehicles and other applications struggle to extract three-dimensional information from two-dimensional monocular images, limiting their ability to accurately determine object dimensions and orientation, which is crucial for navigation and hazard avoidance.
Innovation Solution
A system and method that process monocular images to generate 3D representations by applying a lifting function to regions-of-interest, calculating height, width, camera distance, and rotation, and using these values to create an eight-point box for 3D representation, with a training module to refine the lifting function based on loss calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple cameras are used to capture images, then three-dimensional information can be extracted, but device complexity increases
Solution Approach 1:
The patent applies dimensionality change by transforming 2D image data into 3D spatial representations through a lifting function. Instead of using multiple cameras to capture 3D information directly, the system processes monocular 2D images and lifts them into 3D space, effectively adding a dimensional transformation step that recovers depth and spatial structure from single-viewpoint images.
2Reliability
If monocular images are processed to extract 3D information, then navigation and hazard avoidance improve, but measurement precision deteriorates
Solution Approach 1:
The patent implements feedback through a training module that uses ground truth 3D data to train the lifting function. The system iteratively refines its 3D reconstruction accuracy by comparing predicted 3D representations with known ground truth data, adjusting the lifting function parameters to minimize errors. This feedback loop enables the system to achieve high measurement precision for object dimensions and orientation even when working with monocular images.
Data Source
AI summary
In one embodiment, example systems and methods relate to a manner of generating 3D representations from monocular 2D images. A monocular 2D image is captured by a camera. The 2D image is processed to create one or more feature maps. The features may include depth features, or object labels, for example. Based on the image and the feature map, regions-of-interest corresponding to vehicles in the image are determined. For each region-of-interest a lifting function is applied to the region-of-interest to determine values such as height and width, camera distance, and rotation. The determined values are used to create an eight-point box that is a 3D representation of the vehicle depicted by the region-of-interest. The 3D representation can be used for a variety of purposes such as route planning, object avoidance, or as training data, for example.