Neural Network Inpainting for 3D Point Cloud Colorization
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Solution Overview
Problem
Existing methods for colorizing 3D point clouds face challenges due to misalignment between LiDAR and RGB sensors, leading to artificial occlusions and incorrect color assignments, especially when obstructions like moving objects or poor lighting conditions are present, resulting in inaccurate RGB features.
Innovation Solution
A computer-implemented method using a neural network to combine intensity values and color information from LiDAR and RGB data in a common aligned space, identifying and correcting content discrepancies, and assigning synthetic RGB features to affected points or pixels, thereby improving inpainting accuracy and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If LiDAR and RGB sensors are installed at different locations on the device, then practical installation requirements are met, but parallax effect creates artificial occlusions and wrong RGB features are assigned to LiDAR point cloud
Solution Approach 1:
The patent introduces a neural network as an intermediary component that processes both LiDAR point cloud data and RGB image data separately, then fuses them to generate corrected RGB features. This neural network intermediary learns the complex relationship between the two sensor modalities and compensates for parallax-induced misalignments, allowing the system to maintain installation flexibility while achieving high measurement precision.
2Area of stationary object
If LiDAR and RGB acquisitions are performed asynchronously to cover wide area, then measurement coverage is improved, but moving obstacles cause significant RGB projection artifacts
Solution Approach 1:
The patent performs preliminary alignment and registration of LiDAR and RGB data before the actual measurement process. By pre-establishing the spatial relationship between sensors and preparing the data structure in advance, the system can handle asynchronous acquisitions without suffering from moving obstacle artifacts, maintaining both wide coverage and high reliability.
Solution Approach 2:
The neural network incorporates feedback mechanisms that continuously adjust the RGB feature assignment based on the consistency between LiDAR point cloud and RGB image data. When moving obstacles cause projection artifacts, the feedback loop detects the inconsistency and corrects the RGB features, ensuring reliable measurements even during asynchronous wide-area surveying.
3Productivity
If simple inpainting methods are used to fill missing color data, then processing speed is improved, but incorrect or inaccurate color assignment occurs
Solution Approach 1:
The patent replaces traditional mechanical or algorithmic inpainting methods with a neural network-based approach. The neural network learns from training data the complex patterns and relationships in 3D scenes, enabling it to accurately infer missing color information by understanding contextual relationships between points, surfaces, and objects, thereby achieving both speed and accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method provides improved and accurate RGB features by correcting misalignments and anomalies, ensuring reliable colorization even in the presence of obstructions or poor lighting, resulting in enhanced visualization and data quality for 3D point clouds.
Implementation Method 1
The distances may be calculated with the travel time measurement (time-of-flight) method by observing the time between sending out and receiving a signal
Implementation Method 2
deriving, by a neural network, for each of the identified points or pixels, respectively, colourising information from joint evaluation of the combined intensity values and colour information
Data Source
AI summary
A computer-implemented method and computer system for colourising a 3D point cloud of a setting, the method comprising acquiring point cloud data and image data of the setting, wherein the point cloud data comprises coordinates and an intensity value for each point of the point cloud, and the image data provides colour information of the setting, wherein the method further comprises combining the intensity values and the colour information in a common aligned space, identifying points of the point cloud and/or pixels in the image data that are affected by content discrepancies or anomalies between the colour information and the point cloud data, a neural network deriving, for each of the identified points and/or pixels, colourising information from joint evaluation of the combined intensity values and colour information, and assigning to each of the identified points and/or pixels, the respective colourising information.