Point Cloud Moving Object Removal via Voxel Clustering
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Solution Overview
Problem
Existing point cloud data processing methods rely heavily on manual operations and complex calculations, failing to accurately recognize and remove moving objects from urban environments, leading to ghosting in three-dimensional models.
Innovation Solution
A method and apparatus for processing point cloud data that automatically identifies moving objects by clustering points, detecting ground objects, and selecting candidate objects based on characteristics such as size ratio, smoothness, reflection intensity, and point cloud density, without manual processing, to eliminate ghosting and isolate stationary objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processing by modellers is used to remove moving objects from point cloud data, then moving objects can be removed, but the processing efficiency is low and labor intensity is high
Solution Approach 1:
The patent replaces manual mechanical processing by modellers with an automated computer-based system that uses image recognition algorithms and point cloud analysis to automatically identify and remove moving objects, thereby maintaining accuracy while dramatically improving processing efficiency
Solution Approach 2:
The system enables self-service processing by automatically analyzing point cloud data, identifying moving objects through clustering and recognition algorithms, and removing them without requiring manual intervention, thus eliminating labor intensity while maintaining reliable results
2Productivity
If image recognition is used to determine moving objects, then automated processing is achieved, but the recognition accuracy is insufficient and calculations are complicated
Solution Approach 1:
The patent segments the point cloud data into multiple frames and further clusters points into voxel clusters, breaking down the complex recognition task into manageable units that can be analyzed individually, improving both automation and accuracy
Solution Approach 2:
The patent transitions from traditional two-dimensional image recognition to three-dimensional point cloud analysis by clustering points into voxel clusters with spatial coordinates, adding depth information and enabling more accurate moving object identification in the third dimension
3Ease of manufacture
If traditional image data processing is used, then processing can be performed, but ghosting occurs in three-dimensional models and moving objects cannot be accurately isolated
Solution Approach 1:
The patent replaces traditional two-dimensional image processing with three-dimensional point cloud processing using voxel clusters, enabling accurate spatial analysis and moving object isolation that eliminates ghosting artifacts in the final three-dimensional model
Solution Approach 2:
By moving from 2D image processing to 3D point cloud analysis with voxel clusters containing x, y, z coordinates, the system achieves accurate spatial separation of moving objects from stationary background, eliminating ghosting while maintaining processing feasibility
Data Source
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AI summary
Exemplary implementations of the present disclosure provide a method and apparatus for processing point cloud data. Specifically, the method for processing point cloud data is provided, including: acquiring a first frame and a second frame respectively from the point cloud data; extracting a first candidate object from the first frame and a second candidate object corresponding to the first candidate object from the second frame, respectively; determining a first location of the first candidate object and a second location of the second candidate object in a coordinate system of the point cloud data, respectively; and identifying any one of the first candidate object and the second candidate object as a moving object, in response to an offset between the first location and the second location. According to the exemplary implementations of the present disclosure, a corresponding apparatus, device, and computer storage medium for processing point cloud data are also provided.