Visual Pose Estimation via Key Point Selection and 3D Regression
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Solution Overview
Problem
Current visual localization methods, whether structure-based or deep learning-based, face challenges in achieving accurate and efficient pose determination for image capturing devices, with structure-based methods being memory-intensive and prone to noise, and deep learning methods offering lower accuracy and requiring large datasets.
Innovation Solution
A data processing apparatus and method that uses a deep learning approach to determine the pose of an image capturing device from a single image, selecting key 2D points based on confidence scores, concatenating feature vectors with images to estimate 3D points directly, and employing the Perspective-n-Point scheme with RANSAC to reduce outliers and runtime, eliminating the need for 3D models and large memory storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If structure based methods are used to extract features and match descriptors to obtain correspondences, then localization accuracy can be achieved, but memory occupation increases significantly due to storing 3D models and the expensive descriptor matching procedure
Solution Approach 1:
The patent extracts only the essential 3D point cloud data needed for localization, eliminating the need to store complete 3D models. By using a lightweight point cloud representation and direct 3D point regression from 2D images, the system achieves accurate localization while significantly reducing memory requirements compared to traditional structure-based methods that store comprehensive 3D models and descriptor databases.
Solution Approach 2:
Instead of storing and matching complex descriptors against large 3D models, the patent creates a simplified copy of the scene as a point cloud and directly regresses 3D points from 2D image keypoints. This copying approach maintains localization accuracy while dramatically reducing the data storage burden associated with traditional descriptor matching pipelines.
2Measurement precision
If structure based methods perform descriptor matching to obtain correspondences, then pose estimation can be achieved, but runtime increases due to the expensive and time consuming matching procedure
Solution Approach 1:
The patent replaces the mechanical descriptor matching process with a neural network-based direct regression system. Instead of computationally intensive feature matching between 2D images and 3D models, the system uses a trained neural network to directly regress 3D point coordinates from 2D image keypoints, achieving comparable or superior accuracy with significantly reduced runtime.
Solution Approach 2:
The patent performs preliminary training of the neural network offline using paired 2D-3D data. Once trained, the network can rapidly perform pose estimation without requiring runtime descriptor matching. This preliminary action transfers the computational burden from runtime operations to offline training, enabling real-time localization performance.
3Productivity
If deep learning pose regressors are used to regress pose directly from images, then runtime is reduced and real-time performance is achieved, but localization accuracy becomes lower compared to structure based methods
Solution Approach 1:
The patent merges the advantages of both structure-based and deep learning approaches by combining direct 3D point regression (from deep learning) with PnP pose solving (from structure-based methods). The neural network regresses 3D points from 2D keypoints, and these points are then fed into a PnP solver to compute the final pose, achieving both real-time performance and high accuracy.
Solution Approach 2:
The patent changes the output parameters of the neural network from direct pose estimation to 3D point cloud coordinates. This parameter transformation allows the network to learn a more robust representation of the scene geometry, which can then be accurately transformed into pose estimates using PnP methods, improving both accuracy and real-time performance.
4Reliability
If a large number of correspondences are used in RANSAC scheme to improve pose estimation reliability, then accuracy improves, but the number of iterations increases resulting in increased runtime
Solution Approach 1:
The patent uses a minimal sufficient set of correspondences (e.g., 4-5 point pairs) rather than attempting to process all available correspondences. By carefully selecting a small number of high-quality keypoint matches and using them with PnP and RANSAC, the system achieves reliable pose estimation without the computational overhead of processing excessive correspondences through multiple RANSAC iterations.
Data Source
AI summary
A data processing apparatus for determining a pose of an image capturing device based on an image of a three dimensional (3D) scene is disclosed. The data processing apparatus comprises a processing circuitry configured to: select a plurality of key two dimensional (2D) points of a plurality of 2D points of the image based on a respective score of each of the plurality of 2D points; determine at least for a subset of the plurality of 2D points of the image a respective feature vector for obtaining a plurality of feature vectors; concatenate the image with the plurality of feature vectors for obtaining an intermediate tensor; determine a plurality of 3D points of the 3D scene based on the intermediate tensor; and determine the pose based on the plurality of key 2D points of the image and the plurality of 3D points of the 3D scene using a Perspective-n-Point scheme.