Camera Pose Estimation Using Vanishing Point Guide Filters
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Solution Overview
Problem
Existing camera pose estimation techniques face challenges in accuracy due to image distortion and the vanishing point bounding phenomenon, especially while a vehicle is in motion.
Innovation Solution
A method and apparatus that utilize a vanishing point guide filter to improve camera pose estimation accuracy. This involves dividing an input image into regions, extracting representative features using filters based on camera design values, and calculating feature errors to estimate the camera pose.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vanishing point detection is used for camera pose estimation, then the estimation can be performed using line segments in the image, but the accuracy deteriorates due to image distortion and vanishing point bounding phenomenon
Solution Approach 1:
The image is divided into multiple regions, and for each region, representative features are extracted using filters based on camera design values. This segmentation approach allows the system to handle image distortion and vanishing point bounding by processing local regions independently rather than relying on global vanishing point detection.
Solution Approach 2:
The patent changes the approach from direct vanishing point detection to using filters based on camera design values (intrinsic and extrinsic parameters) to extract representative features. This parameter-based filtering approach is more robust to image distortion and vehicle motion than traditional vanishing point methods.
2Ease of operation
If traditional vanishing point detection is used, then the process is simple, but it is difficult to estimate accurate vanishing lines due to vehicle behavior while driving
Solution Approach 1:
The patent uses pre-trained networks and filters based on predetermined camera design values to extract representative features before pose estimation. This preliminary feature extraction using camera-specific parameters prepares the data in a way that is robust to vehicle motion, avoiding the need for complex post-processing of inaccurate vanishing points.
Solution Approach 2:
The patent introduces representative features extracted by camera-design-value-based filters as an intermediary between the raw image and the pose estimation. These representative features serve as a mediator that is less sensitive to vehicle behavior than direct vanishing point detection, improving accuracy while maintaining operational simplicity.
3Adaptability or versatility
If camera pose estimation is performed without considering camera design values, then the process is general, but the accuracy deteriorates when design values of different cameras are used
Solution Approach 1:
The patent applies filters specific to each camera's design values (intrinsic and extrinsic parameters) to extract representative features from different regions of the image. This local customization of feature extraction to each camera's characteristics improves accuracy for each specific camera while maintaining the general framework of the estimation method.
Solution Approach 2:
The patent incorporates camera design values (focal length, optical center, mounting position, etc.) as parameters in the filter functions used for feature extraction. By changing the filter parameters to match each camera's specific design values, the method achieves high accuracy for each camera while remaining a generalizable approach applicable to any camera with known design parameters.
Data Source
AI summary
Disclosed are a method and apparatus for estimating a camera pose. The method includes dividing an input image received from a camera into a plurality of regions, dividing the plurality of regions into a first detection zone for detecting a vanishing point or a roll and a second detection zone for detecting the roll, extracting a representative feature from features of the input image by using a filter generated in advance for each of the plurality of regions based on a design value of the camera, calculating a feature error of each of the first detection zone and the second detection zone based on the representative feature, and estimating a pose of the camera based on the feature error.