Neural Multi-Camera Depth Modeling Without Camera Calibration
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Solution Overview
Problem
Autonomous vehicles and robotic systems face challenges in accurately perceiving their environment due to limitations in sensor data, such as depth ambiguities, limited resolution, and image artifacts, which hinder their ability to develop a comprehensive understanding of their surroundings and perform tasks like navigation.
Innovation Solution
A self-supervised depth estimation method using a neural camera model that predicts depth maps from image frames captured by multiple cameras without requiring known camera models or intrinsics, allowing for depth estimation across multiple frames and improving situational awareness by warping and projecting images to align with different camera perspectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multiple cameras are used to capture images from different locations, then the comprehensive awareness of the surrounding environment is improved, but inconsistencies and errors arise in areas observed by multiple cameras at different locations
Solution Approach 1:
The patent combines images from multiple cameras mounted at different locations on the vehicle into a unified depth map. The system processes images from multiple camera perspectives and merges them to create a comprehensive environmental understanding, resolving the contradiction by integrating multiple information sources while maintaining consistency through the neural network's unified processing approach.
Solution Approach 2:
The neural network acts as an intermediary that processes and reconciles images from multiple cameras. It learns to handle the inconsistencies between different camera perspectives by training on the relationship between images from various locations, using the neural network architecture as a mediator to produce consistent depth estimates across all camera views.
2Device complexity
If a neural camera model is used to predict depth maps without known camera models or intrinsics, then the complexity of calibration is reduced, but the measurement precision of depth estimation is affected
Solution Approach 1:
The neural camera model performs self-calibration by learning camera intrinsics and extrinsics directly from image data without requiring manual calibration procedures. The system serves itself by automatically adapting to different camera configurations and mounting locations, eliminating the need for known camera models while maintaining depth estimation accuracy through learned parameters.
Solution Approach 2:
The patent changes the approach from using fixed, known camera parameters to learning camera parameters dynamically. The neural network learns and adapts camera intrinsics and extrinsics as part of the training process, allowing the system to work with unknown or varying camera configurations while maintaining measurement precision through data-driven parameter optimization.
3Measurement precision
If depth estimation is performed using traditional methods with known camera models, then the measurement precision is improved, but the adaptability to different camera configurations is reduced
Solution Approach 1:
The neural camera model creates a universal system that can handle multiple camera configurations, mounting locations, and camera types without requiring re-calibration or specific known models. The single neural network architecture serves multiple functions by adapting to different vehicle types, camera arrangements, and perspectives, maintaining precision across diverse configurations.
Data Source
AI summary
Systems and methods for self-supervised depth estimation using image frames captured from a plurality of cameras mounted on a vehicle, may include: receiving a first image from a camera mounted at a first location on the vehicle, the source image comprising pixels representing a scene of the environment of the vehicle; receiving a reference image from a camera mounted at a second location on the vehicle, the reference image comprising pixels representing a scene of the environment; predicting a depth map for the first image, the depth map comprising predicted depth values for pixels of the first image; warping the first image to a perspective of the camera mounted at the second location on the vehicle to arrive at a warped first image; projecting the warped first image onto the source image; determining a loss based on the projection; and updating the predicted depth values for the first image.