NeRF Inversion for Efficient RGB Camera 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, and often struggle with real-world scenarios due to resolution, frame rate, field of view, and depth range limitations.
Innovation Solution
Invert a trained Neural Radiance Field (NeRF) model to estimate pose using gradient descent, selectively rendering only a small number of synthetic pixels and employing strategies like interest region sampling to optimize camera pose efficiently.
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 applies universality by enabling a standard RGB camera to perform pose estimation that traditionally required specialized RGB-D cameras. The NeRF model learns to infer depth and 3D structure from standard color images, making the system compatible with ubiquitous RGB cameras while achieving comparable pose estimation accuracy to specialized sensors.
Solution Approach 2:
The patent substitutes the mechanical/optical depth sensing mechanism of RGB-D cameras with a data-driven neural network approach. Instead of using active infrared projectors and depth sensors, the system uses a trained NeRF model that processes standard RGB images to estimate camera pose, replacing specialized hardware with a software-based solution.
2Productivity
If traditional pose estimation methods are used, then device complexity is reduced, but productivity and computational efficiency worsen
Solution Approach 1:
The patent applies preliminary action by pre-training the NeRF model offline using a dataset of images with known poses. This training phase captures the complex relationships between image content and camera pose, storing this knowledge in the neural network weights. During actual pose estimation, the pre-learned model quickly processes new images without requiring complex real-time computations, achieving both speed and accuracy.
3Measurement precision
If high resolution and full field of view are used, then measurement precision is improved, but use of energy and computational cost increase
Solution Approach 1:
The patent applies partial action by processing only the necessary features from the input image for pose estimation. The NeRF model selectively attends to relevant visual features and regions that provide information about camera pose, rather than processing the entire high-resolution image in detail. This reduces computational energy consumption while maintaining sufficient measurement precision for accurate pose estimation.
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.