Virtual Camera System for 3D Point Cloud Text Recognition
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Solution Overview
Problem
Current 2D text detection and recognition technologies face challenges in 3D spaces due to limited 3D data resources and high computational complexity, making text localization and recognition in point cloud environments inefficient and inaccurate, especially with skewed or occluded views.
Innovation Solution
A method utilizing a virtual camera system that generates synthetic images by orthogonal projection from a 3D point cloud, correlating each point with a pixel in a 2D panoramic image, allowing for improved text recognition by rotating the virtual camera to optimize viewing angles and reduce computational demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If geometric feature extraction methods are used to improve text recognition accuracy in 3D environments, then recognition accuracy improves, but computational complexity increases significantly
Solution Approach 1:
The patent creates synthetic 2D images from the 3D point cloud data using virtual camera projections. These synthetic images are copies of the 3D scene rendered in 2D, allowing standard 2D OCR systems to process them. This approach copies the essential visual information from 3D to 2D space, enabling accurate text recognition without requiring complex 3D feature extraction algorithms.
Solution Approach 2:
The patent replaces complex geometric feature extraction and 3D text recognition mechanisms with a simpler virtual camera projection system. Instead of using sophisticated 3D computer vision algorithms to directly recognize text in point clouds, the system substitutes this with a rendering approach that projects 3D data onto 2D image planes, which can then be processed by conventional 2D OCR systems.
2Reliability
If multiple viewing angles are generated to handle skewed and occluded text views, then recognition robustness improves, but processing time increases
Solution Approach 1:
The patent pre-generates multiple synthetic images from different virtual camera angles and positions before performing text recognition. By creating these multiple viewing angles in advance, the system ensures that at least one view will provide a clear, unoccluded view of the text, improving recognition robustness. This preliminary generation of alternative views allows the system to handle skewed and occluded text effectively without requiring real-time angle optimization during recognition.
3Measurement precision
If 3D point cloud data is directly processed for text recognition, then spatial accuracy is maintained, but computational efficiency decreases
Solution Approach 1:
The patent transforms 3D point cloud data into 2D synthetic images through virtual camera projections. This dimensionality reduction from 3D to 2D allows standard 2D image processing and OCR algorithms to be applied, significantly improving computational efficiency. The one-to-one grid map construction ensures that spatial relationships are preserved during this transformation, maintaining spatial accuracy while enabling faster processing with conventional 2D techniques.
Data Source
AI summary
A Method and System for Utilizing Virtual Cameras in Point Cloud Environments to Support Computer Vision Object Recognition. More specifically, a method of object recognition with a virtual camera, comprising providing a three-dimensional point cloud and an associated two-dimensional panoramic image, each comprising at least one object of interest, constructing a one-to-one grid map, performing detection and localization on the object of interest, constructing a 3D bounding box around the object of interest, forming a virtual camera system around the bounding box oriented towards the object of interest, rotating the virtual camera around the bounding box, calculating a recognition score for each of the plurality of synthetic images, determining a best angle based on the recognition score, generating a best synthetic image based on the best angle, and obtaining an object recognition prediction based on the best synthetic image. Additionally, a method of text recognition with a virtual camera.