Camera Intrinsic Self-Calibration From Image Sequences
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Solution Overview
Problem
Conventional camera calibration methods are laborious and require specialized data collection, necessitating repeated calibration in applications like mobile robots and autonomous vehicles due to frequent changes in camera parameters.
Innovation Solution
A self-supervised learning system that processes sequences of images using depth and pose networks to estimate and update camera intrinsic parameters, enabling automatic recalibration and rectification of images to correct distortion, employing differentiable unprojection and projection operations within a Unified Camera Model framework.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional camera calibration methods are used, then accurate camera intrinsic parameters can be obtained, but the calibration process becomes laborious and requires specialized data collection
Solution Approach 1:
The system performs self-calibration by automatically estimating camera intrinsic parameters from video sequences captured by the camera itself, without requiring external calibration tools or specialized data collection. The calibration module uses the camera's own image data to iteratively optimize intrinsic parameters through photometric loss minimization, enabling the system to calibrate itself autonomously
Solution Approach 2:
The system changes the optimization approach from traditional manual parameter adjustment to automated gradient-based optimization. It iteratively updates camera intrinsic parameters (focal length, principal point, distortion coefficients) by minimizing photometric loss between projected and actual image features, allowing continuous parameter refinement without human intervention
2Measurement precision
If conventional camera calibration is performed, then camera parameters can be determined, but the process needs to be repeated frequently when camera parameters change in mobile applications
Solution Approach 1:
The system performs continuous calibration by processing video sequences in real-time or near-real-time as they are captured. Instead of periodic recalibration, the calibration module continuously optimizes camera intrinsic parameters throughout operation, ensuring up-to-date parameters without stopping the system or performing separate calibration sessions
Solution Approach 2:
The camera system automatically detects and adapts to parameter changes by continuously monitoring image data and adjusting intrinsic parameters as needed. This self-adjusting capability eliminates the need for manual intervention when camera parameters drift due to environmental factors or physical changes in mobile applications
3Manufacturing precision
If manual calibration procedures are used, then camera distortion can be corrected, but the process requires specialized equipment and careful tuning
Solution Approach 1:
The system uses the camera's own captured images to determine distortion characteristics and correction parameters. By analyzing natural image structures and geometric relationships in the captured video sequences, the system derives distortion coefficients without requiring external calibration patterns, chessboards, or specialized test equipment
Solution Approach 2:
The system replaces mechanical calibration procedures (physical calibration tools, manual adjustment mechanisms) with computational methods. It uses differentiable rendering and photometric loss optimization to automatically determine distortion parameters and generate rectification transforms through software-based image processing rather than hardware-based calibration
Data Source
AI summary
Systems and methods described herein relate to self-supervised learning of camera intrinsic parameters from a sequence of images. One embodiment produces a depth map from a current image frame captured by a camera; generates a point cloud from the depth map using a differentiable unprojection operation; produces a camera pose estimate from the current image frame and a context image frame; produces a warped point cloud based on the camera pose estimate; generates a warped image frame from the warped point cloud using a differentiable projection operation; compares the warped image frame with the context image frame to produce a self-supervised photometric loss; updates a set of estimated camera intrinsic parameters on a per-image-sequence basis using one or more gradients from the self-supervised photometric loss; and generates, based on a converged set of learned camera intrinsic parameters, a rectified image frame from an image frame captured by the camera.