Neural Camera Models for Calibration-Free 3D Keypoint Lifting
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Solution Overview
Problem
Conventional visual odometry systems face challenges in accurately determining depth and ego-motion without prior knowledge of camera models, especially with non-pinhole camera geometries, leading to reduced situational awareness and navigation difficulties.
Innovation Solution
The implementation of a neural camera model that learns to lift 2D keypoints to 3D and project them back without requiring knowledge of the camera model, enabling self-supervised learning for depth and pose estimation across various camera geometries, including pinhole, fisheye, and catadioptric models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If parametric distortion models or complex lens design models (e.g., fisheye cameras) are used, then measurement precision and adaptability improve, but device complexity and ease of manufacture deteriorate
Solution Approach 1:
The patent replaces traditional parametric distortion models and complex lens design models with a neural network-based camera model. This neural model learns to perform 2D-to-3D lifting and 3D-to-2D projection operations directly from data, substituting the need for explicit mathematical distortion models and calibration parameters. The neural network is trained on synthetic data generated from various camera geometries, enabling it to generalize across pinhole, fisheye, and catadioptric models without requiring model-specific complexity.
2Measurement precision
If conventional visual odometry with known camera models is used, then measurement precision improves, but adaptability deteriorates
Solution Approach 1:
The patent creates a universal camera model based on neural networks that can handle multiple camera geometries (pinhole, fisheye, catadioptric) with a single unified architecture. The neural network is trained on synthetic data from various camera types, enabling it to perform 2D-to-3D lifting and 3D-to-2D projection operations across different camera configurations without requiring separate models or calibration for each type, thus achieving both precision and versatility.
3Measurement precision
If individual custom projection models are created for each camera type, then measurement precision improves, but ease of operation and adaptability deteriorate
Solution Approach 1:
The patent implements a self-service approach where the neural network camera model automatically adapts to different camera geometries through its training process. When deployed with new camera data, the model can be fine-tuned using self-supervised learning from real images without requiring manual calibration or model specification. The system serves itself by learning the projection characteristics directly from data, eliminating the need for operators to manually configure or calibrate camera models for each deployment scenario.
Data Source
AI summary
Systems and methods for self-supervised learning for visual odometry using camera images captured on a camera, may include: using a key point network to learn a keypoint matrix for a target image and a context image captured by the camera; using the learned descriptors to estimate correspondences between the target image and the context image; based on the keypoint correspondences, lifting a set of 2D keypoints to 3D, using a learned neural camera model; estimating a transformation between the target image and the context image using 3D-2D keypoint correspondences; and projecting the 3D keypoints into the context image using the learned neural camera model.