Neural Camera Models for Visual Odometry Across Lens Geometries
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Solution Overview
Problem
Existing autonomous vehicle and robotic systems face challenges in determining depth and navigating environments due to limitations in sensor perspectives and camera models that do not adhere to the pinhole assumption, leading to reduced situational awareness and navigation difficulties.
Innovation Solution
A neural camera model is used to predict a pixel-wise ray surface for images, enabling self-supervised learning of depth and pose estimates across various camera geometries, including pinhole, fisheye, and catadioptric lenses, without requiring camera model knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If parametric distortion models or complex lens models (e.g., fisheye cameras) are used, then the camera can accurately represent complex lens distortion, but the model becomes significantly more complex and difficult to calibrate, requiring individual creation for each camera type
Solution Approach 1:
The patent applies universality by creating a single neural camera model architecture that can handle multiple camera types (pinhole, fisheye, catadioptric) and various distortion models through learned parameters rather than requiring separate specialized models for each camera type. The neural network learns camera-specific characteristics during training, making the system universally applicable across different camera geometries.
Solution Approach 2:
The patent replaces traditional parametric distortion models with a neural network-based model that learns the mapping from image coordinates to ray directions. This substitution eliminates the need for manual calibration and complex mathematical distortion models, replacing them with a data-driven approach that automatically adapts to different camera types.
2Device complexity
If traditional pinhole camera model is used, then the model is simple and allows closed form lifting and projection operations, but it does not hold for fisheye, catadioptric lenses or physical arrangements that break the pinhole assumption
Solution Approach 1:
The neural camera model serves as a universal framework that can represent pinhole, fisheye, catadioptric, and other camera geometries through learned parameters. Instead of requiring different models for different camera types, the single neural architecture adapts to various geometries by learning the appropriate ray surface representation during training.
Solution Approach 2:
The patent changes the parameter representation from fixed geometric parameters (focal length, principal point) to learned neural network parameters that capture the ray surface. This allows the model to flexibly represent different camera geometries by adjusting learned parameters rather than being constrained by the pinhole assumption.
3Measurement precision
If specialized architectures are created for specific camera models, then the model can be optimized for that specific camera type, but the architecture must be significantly modified before it can be used to train on a new dataset from a novel camera
Solution Approach 1:
The patent creates a universal neural camera model that can be trained on data from any camera type without requiring architectural modifications. The model learns camera-specific characteristics during training while maintaining the same underlying architecture, enabling seamless transfer across different camera geometries and datasets.
4Ease of manufacture
If monocular cameras are used, then the approach is cost-effective, but the sensor data does not explicitly include depth information and requires processing routines to derive depth
Solution Approach 1:
The patent replaces traditional depth estimation processing routines with a neural network that directly predicts depth and ray surfaces from monocular images. This substitution enables the system to derive depth information more effectively from single-camera input, maintaining cost effectiveness while improving depth estimation capability.
Data Source
AI summary
Systems and methods for self-supervised learning for visual odometry are provided. An example method may comprise: (1) using a keypoint network to generate a keypoint matrix for a target image captured by a camera and a keypoint matrix for a context image captured by the camera, each keypoint matrix comprising keypoints of its respective image; (2) using a neural camera model to predict a pixel-wise ray surface for the target image, wherein the predicted pixel-wise ray surface associates a respective pixel in the target image with a corresponding direction; (3) using the generated keypoint matrices and the predicted pixel-wise ray surface to: (a) lift 2D keypoints of the target image to 3D keypoints, and (b) project the 3D keypoints of the target image into the context image; and (4) computing a geometric loss based on differences between the projected keypoints of the target image and the keypoints of the context image.