Lidar-Based Object Removal for Environmental Rendering
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Solution Overview
Problem
Current image processing techniques for removing occluding objects from environmental renderings are limited by precision issues such as visual distortion, glare, and shadows, leading to false negatives and false positives, making it difficult to accurately identify and exclude objects like vehicles and pedestrians from images.
Innovation Solution
The use of lidar data to classify objects as moving or stationary, which guides the omission of objects from the rendering by generating a lidar point cloud and translating it into three-dimensional space for accurate object classification and exclusion, thereby improving the precision of object removal in image processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If image processing techniques are used to identify and remove occluding objects, then object removal can be achieved, but measurement precision deteriorates due to visual distortion, glare, and shadows causing false negatives and false positives
Solution Approach 1:
The patent introduces lidar data as an intermediary component to assist image processing in identifying occluding objects. The lidar point cloud provides accurate three-dimensional spatial information about objects, serving as a mediator that bridges the gap between visual image data and precise object identification. This intermediary data source enables the system to overcome the limitations of image-only processing by providing independent verification of object presence, position, and depth, thereby resolving the precision problems caused by visual distortion, glare, and shadows
Solution Approach 2:
The patent merges lidar data with image processing techniques to create a hybrid object identification system. By combining the spatial accuracy of lidar point cloud data with the visual recognition capabilities of image processing, the system achieves more reliable occluding object detection. The fusion of these two data sources allows the system to cross-validate object identification results, reducing false positives and false negatives that would occur when using either technique alone
2Measurement precision
If lidar data is used to classify and identify objects in three-dimensional space, then object identification precision is improved, but device complexity increases due to the need for lidar emitter and detector integration
Solution Approach 1:
The patent applies multi-functionality by utilizing lidar data for multiple purposes: object detection, three-dimensional spatial mapping, and verification of image processing results. The same lidar point cloud that provides precise object location information also serves as a reference framework for understanding the three-dimensional environment, eliminating the need for separate systems and reducing overall complexity despite the advanced capabilities enabled
Solution Approach 2:
The patent creates a three-dimensional copy of the environment through lidar point cloud generation. This digital twin or virtual representation of the physical space provides accurate spatial relationships and object positions without requiring physical measurement instruments. The copied three-dimensional data can be processed and analyzed computationally, reducing the need for complex physical measurement devices while maintaining high precision in object identification
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
This approach enhances the accuracy of object identification and exclusion by leveraging lidar data to distinguish between moving and stationary objects, reducing false positives and negatives and providing a more precise rendering of the environment without occluding objects.
Implementation Method 1
some image capturing vehicles are also equipped with a lidar emitter that emits a low-powered, visible-spectrum laser at a specific wavelength, and a lidar detector that detects light at the specific wavelength representing a reflection off of nearby objects
Data Source
AI summary
In scenarios involving the capturing of an environment, it may be desirable to remove temporary objects (e.g., vehicles depicted in captured images of a street) in furtherance of individual privacy and/or an unobstructed rendering of the environment. However, techniques involving the evaluation of visual images to identify and remove objects may be imprecise, e.g., failing to identify and remove some objects while incorrectly omitting portions of the images that do not depict such objects. However, such capturing scenarios often involve capturing a lidar point cloud, which may identify the presence and shapes of objects with higher precision. The lidar data may also enable a movement classification of respective objects differentiating moving and stationary objects, which may facilitate an accurate removal of the objects from the rendering of the environment (e.g., identifying the object in a first image may guide the identification of the object in sequentially adjacent images).