Multi-Camera 3D Perception Through Direct Location Regression
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Solution Overview
Problem
Conventional systems for determining 3D locations of objects within environments face inaccuracies due to occlusions, camera misalignments, and calibration issues, particularly in complex environments with numerous cameras and occluded spaces, leading to reduced accuracy in object tracking.
Innovation Solution
A three-dimensional multi-camera perception system processes image data using feature extractors and spatio-temporal transformers to directly determine 3D locations of objects, eliminating the need for initial 2D location projection by leveraging multi-view image features and calibration data to generate bird's-eye-view features, which are then fused and decoded to achieve precise 3D information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems project 2D locations to 3D coordinate space using calibration information, then the system can determine 3D locations of objects, but projection errors occur due to occlusions, inaccurate calibration, and misalignment across cameras
Solution Approach 1:
Instead of projecting 2D image locations to 3D space (conventional approach), the patent inverts the process by directly regressing 3D locations from image data using neural networks. This avoids the projection step that causes errors from occlusions, calibration inaccuracies, and misalignment, thereby improving both measurement precision and reliability of 3D location determination
Solution Approach 2:
The patent replaces the traditional geometric projection mechanism with a data-driven neural network regression mechanism. The neural networks learn to directly predict 3D locations from image inputs, substituting the mechanical/mathematical projection process that is sensitive to calibration errors and occlusions, thus improving projection accuracy and overall 3D location reliability
2Area of stationary object
If multiple cameras are deployed throughout complex environments to capture object data, then the system can track objects in large spaces, but occlusions and misalignments increase projection errors
Solution Approach 1:
The patent inverts the conventional projection approach by using direct 3D location regression from image data. This inversion eliminates the accumulation of errors from multiple cameras' calibration and alignment issues, allowing the system to maintain high measurement precision even when covering large complex environments with numerous cameras
Solution Approach 2:
The patent changes the fundamental parameters of the 3D location determination process by using neural networks to directly predict 3D coordinates from image data, rather than relying on traditional geometric projection parameters. This parameter change enables the system to handle complex multi-camera environments more effectively, maintaining accuracy across large covered areas despite occlusions and alignment challenges
Data Source
AI summary
In various examples, three-dimensional multi-camera perception systems and applications is described herein. Systems and methods are disclosed herein that process image data generated using multiple cameras located throughout an environment in order to directly determine three-dimensional (3D) information associated with objects located within the environment. For instance, the image data may be processed using one or more feature extractors (e.g., one or more backbones) to determine multi-view image features associated with images represented by the image data. These multi-view image features, along with calibration data associated with the cameras, may then be processed using one or more spatio-temporal transformers (e.g., one or more spatial encoders, one or more temporal encoders, etc.) in order to determine 3D locations of objects within the environment.