Autoregressive Camera Pose Estimation With Lower Compute Load
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Solution Overview
Problem
Identifying the pose of a camera requires significant memory, time, and computing resources, often leading to inaccurate results.
Innovation Solution
Utilizing neural networks for camera pose identification, specifically through autoregressive models that leverage previous pose predictions to efficiently determine current camera poses, reducing resource utilization while improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to identify camera pose, then comprehensive pose information can be obtained, but significant memory, time, and computing resources are consumed and accuracy is reduced
Solution Approach 1:
The system performs preliminary pose predictions using neural networks before final pose identification. By predicting poses in advance and using these predictions to guide subsequent processing, the system reduces the computational resources needed for accurate pose identification while maintaining or improving accuracy.
Solution Approach 2:
The patent introduces intermediate representations and predictive models as mediators between raw image data and final pose identification. These intermediaries process and transform data in a way that reduces computational complexity and resource requirements while preserving essential pose information.
2Loss of time
If traditional pose identification methods are used, then accurate pose information can be obtained, but significant memory and time resources are consumed
Solution Approach 1:
The system performs preliminary pose predictions using neural networks before final pose identification. By predicting poses in advance and using these predictions to guide subsequent processing, the system reduces the computational resources needed for accurate pose identification while maintaining or improving accuracy.
Solution Approach 2:
The patent employs sequential processing where pose predictions from previous frames are continuously updated and refined. This continuous action allows the system to maintain accurate pose tracking over time while reducing the computational burden on individual frames through temporal coherence.
3Reliability
If comprehensive processing is applied to identify camera pose, then pose information can be obtained, but computing resources are significantly consumed
Solution Approach 1:
The system performs preliminary pose predictions using neural networks before final pose identification. By predicting poses in advance and using these predictions to guide subsequent processing, the system reduces the computational resources needed for accurate pose identification while maintaining or improving accuracy.
Solution Approach 2:
The patent transforms the pose identification problem by changing parameters and representations. By using predictive models and intermediate representations, the system processes data in a more energy-efficient manner while maintaining reliable pose identification results.
Data Source
AI summary
Apparatuses, systems, and techniques to identify a pose of one or more cameras based, at least in part, on one or more different poses of the one or more cameras. In at least one embodiment, a pose of a camera for an image of a sequence of images is identified using one or more neural networks, based, at least in part, on one or more identified poses of the camera for one or more previous images of the sequence.