NeRF Pose Estimation Using Geometric Consistency on Unposed Images
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Solution Overview
Problem
Conventional neural radiance field (NeRF) models require known and accurate camera poses for training, failing on smooth or self-similar scenes, and existing pose estimation methods are insufficient for complex real-world scenes, limiting the generation of high-quality images.
Innovation Solution
A system that trains a NeRF model jointly with a pose encoder neural network using a geometric consistency loss, incorporating an equivalence relation to map pose estimates to equivalence classes and penalize deviations from epipolar geometry, enabling accurate image synthesis from unposed images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional pose estimation algorithms are used, then the system can estimate camera poses, but it fails on smooth or self-similar scenes and cannot achieve high quality image synthesis
Solution Approach 1:
The patent introduces an equivalence relation as an intermediary concept to transform the pose estimation problem. Instead of directly estimating poses from images (which fails on smooth/self-similar scenes), the system maps poses to equivalence classes that capture geometric relationships invariant to certain transformations. This intermediary representation makes the estimation more robust and reliable.
Solution Approach 2:
The patent changes the parameter space by transforming pose estimation into an equivalence class mapping problem. By representing poses through equivalence classes rather than direct camera parameters, the system achieves better generalization and reliability on challenging scene types while maintaining measurement precision.
2Adaptability or versatility
If the system uses an encoder neural network with implicit regularization, then it can train on unposed images, but the regularization is insufficient for complex real-world scenes
Solution Approach 1:
The patent implements feedback through the geometric consistency loss that operates on equivalence classes. The loss function provides corrective feedback during training by penalizing deviations from geometric consistency, enabling the system to adapt to complex real-world scenes while maintaining high reconstruction accuracy that implicit regularization alone cannot achieve.
3Ease of manufacture
If conventional NeRF training requires known camera poses, then training is straightforward, but the system cannot handle unposed images and requires more views
Solution Approach 1:
The patent inverts the conventional approach by not requiring known poses as input. Instead, it formulates the problem as learning an equivalence relation from unposed images, where the pose information is implicitly learned through the geometric consistency constraints rather than being explicitly provided during training.
4Productivity
If the system reduces pose estimation to equivalence classes, then the problem becomes more convex and requires fewer views, but the approach is more complex
Solution Approach 1:
The patent uses copying by representing poses through equivalence classes that capture essential geometric relationships. This copying approach allows the system to work with simplified representations that require fewer training views while the underlying complexity is managed through the structured equivalence relation framework.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a neural radiance field (NeRF) model on unposed images. In particular, the training incorporates a geometric consistency loss to train the encoder neural network that predicts the poses of the unposed images.