Neural Radiance Field Inversion for Efficient 6D Pose Estimation
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Solution Overview
Problem
Existing pose estimation techniques require specialized sensors and are computationally expensive, particularly when using RGB-D cameras, limiting their application to lab settings and lacking efficiency in real-world scenarios.
Innovation Solution
Invert a trained Neural Radiance Field (NeRF) model to estimate pose using gradient descent, selectively rendering synthetic pixels and employing strategies like interest region sampling to optimize camera pose estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized sensors (RGB-D cameras) are used for pose estimation, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent creates a neural radiance field model that serves as a virtual copy of the physical scene, allowing pose estimation to be performed in software rather than requiring specialized RGB-D sensors. The trained NeRF model can generate synthetic images from any pose, enabling accurate pose estimation through comparison with actual images without complex hardware
Solution Approach 2:
The patent replaces the mechanical/optical measurement system (RGB-D cameras and specialized sensors) with a computational approach using neural radiance fields. Instead of using specialized hardware to directly measure pose, the system uses a trained neural model to infer pose from standard images through gradient descent optimization
2Measurement precision
If traditional pose estimation methods are used, then measurement precision is improved, but productivity decreases due to high computational cost
Solution Approach 1:
The patent performs preliminary action by training the neural radiance field model offline before actual pose estimation is needed. During runtime, the pre-trained model can quickly estimate pose by comparing images through gradient descent, avoiding the need for computationally expensive real-time processing while maintaining accuracy
Solution Approach 2:
The patent uses interest region sampling to focus computational resources only on relevant parts of the image rather than processing the entire image. This partial action approach reduces computational load significantly while maintaining pose estimation accuracy by concentrating processing on discriminative regions
3Measurement precision
If full image rendering is performed for pose estimation, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent extracts only the essential information needed for pose estimation by using interest region sampling. Instead of rendering and comparing entire images, the system identifies and processes only the discriminative regions that contain useful pose information, dramatically reducing processing time while maintaining accuracy
Solution Approach 2:
The patent applies partial action by selectively processing only interest regions rather than full images. This approach takes less computational action (processing only parts of images) but achieves sufficient precision for pose estimation by focusing on the most informative regions
Data Source
AI summary
Provided are systems and methods that invert a trained NeRF model, which stores the structure of a scene or object, to estimate the 6D pose from an image taken with a novel view, 6D pose estimation has a wide range of applications, including visual localization and object pose estimation for robot manipulation.