Point Cloud Image Perspective Generation Without Camera Synchronization
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Solution Overview
Problem
The acquisition process of image perspectives in point cloud mapping is time-consuming and labor-intensive, requiring complex synchronization systems and expensive cameras, and is affected by environmental factors, leading to low-quality results.
Innovation Solution
Collect point cloud data and multiple image perspectives, determine pose matrices, and use a neural network model to convert point cloud perspectives into image perspectives, eliminating the need for expensive cameras and complex synchronization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expensive cameras (e.g., 360-degree panoramic Ladybug3) are used to acquire high-quality omnidirectional image perspectives, then the quality of image perspective is improved, but the cost increases significantly
Solution Approach 1:
The patent uses point cloud data to generate synthetic image perspectives that copy the visual information captured by expensive cameras. Instead of relying on physical cameras, the system creates virtual images from 3D point cloud representations of the scene, effectively copying the function of expensive imaging equipment through computational methods.
Solution Approach 2:
The patent replaces the mechanical camera system with a computational approach. Instead of using optical lenses, sensors, and complex camera hardware to capture images, the system uses point cloud processing algorithms to generate image perspectives, substituting mechanical imaging with digital reconstruction.
2Measurement precision
If complex synchronization systems are built to coordinate lidar device and camera, then the accuracy of time-space calibration is improved, but the device complexity increases
Solution Approach 1:
The patent extracts and removes the camera and synchronization system from the point cloud mapping process. By using only point cloud data from the lidar device, the system eliminates the need for camera synchronization, time-space calibration, and associated complex hardware, while still generating image perspectives.
Solution Approach 2:
The patent introduces point cloud data as an intermediary that bridges the gap between 3D spatial information and 2D image perspectives. Instead of directly synchronizing cameras with lidar, the system uses point cloud representations as a common intermediate format that can be processed to generate images without requiring temporal synchronization.
3Productivity
If traditional camera-based methods are used to collect image perspectives, then the process is time-consuming and labor-intensive, but the system is simpler in structure
Solution Approach 1:
The patent merges the functions of image capture and 3D mapping into a single point cloud data collection process. By generating image perspectives directly from point cloud data, the system combines what were previously separate operations (camera imaging and lidar scanning) into one unified workflow, improving efficiency without requiring additional hardware.
4Adaptability or versatility
If cameras are used to capture image perspectives in varying environmental conditions, then the adaptability to different scenarios is improved, but the image quality is affected by environmental factors such as weather, illumination and shadow
Solution Approach 1:
The patent uses inexpensive point cloud data as a substitute for expensive, environmentally sensitive camera systems. Point cloud data from lidar is not affected by illumination, shadow, or weather conditions in the same way optical images are, providing a more reliable and cost-effective source for generating image perspectives in varying environmental conditions.
Data Source
AI summary
Provided are a model generation method and apparatus, an image perspective determining method and apparatus, a device, and a medium. The model generation method includes that: point cloud data and multiple image perspectives are collected to obtain coordinate data of the point cloud data and multiple image collection time points; a pose matrix corresponding to each image collection time point is determined, and a point cloud perspective at each image collection time point is generated according to the pose matrix and the coordinate data; and the point cloud perspective at each image collection time point and the respective one image perspective at the each image collection time point are used as a group of training samples, an original neural network model is trained based on multiple groups of training samples, and an image conversion model for converting a point cloud perspective into an image perspective is generated.