3D Point Cloud Volume Estimation Using 2D Grid Bins
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Solution Overview
Problem
Conventional machine vision techniques face inefficiencies when processing massive 3D point clouds, as they lack information about spatial relationships among points, making direct volume computation infeasible and time-consuming.
Innovation Solution
The method involves configuring a 2D grid of bins along a reference plane within the 3D point cloud, determining the number of 3D points and height of each bin, and calculating an estimated volume by multiplying the fill weight, mean height, and common area of each bin.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional machine vision techniques are used to process 3D point clouds, then volume computation can be performed, but the processing time is excessive and the system requires significant processing resources
Solution Approach 1:
The patent divides the 3D point cloud into multiple 2D depth maps by projecting points onto different planes. This segmentation transforms the complex 3D processing task into multiple simpler 2D processing tasks, significantly reducing computational complexity and processing time while maintaining volume computation accuracy.
Solution Approach 2:
The patent converts 3D point cloud data into 2D depth maps through orthogonal projection onto reference planes. This dimensionality reduction from 3D to 2D simplifies the data structure and enables faster processing using 2D image processing techniques, directly addressing the productivity and time loss contradiction.
2Loss of information
If 3D point cloud data is used for volume determination, then comprehensive 3D information is available, but the data lacks information about relations among points making processing complicated
Solution Approach 1:
By projecting 3D points onto 2D planes, the patent creates depth maps where spatial relationships are implicitly encoded in the 2D coordinate system and depth values. This transformation preserves essential spatial information while organizing it in a structure that is much easier to process, reducing processing complexity without significant information loss.
Solution Approach 2:
The patent introduces 2D depth maps as an intermediary representation between the raw 3D point cloud and the final volume calculation. These depth maps serve as a mediator that organizes 3D spatial relationships into a 2D format that is more amenable to standard image processing algorithms, thereby reducing processing complexity.
Data Source
AI summary
The techniques described herein relate to methods, apparatus, and computer readable media configured to determine an estimated volume of an object captured by a three-dimensional (3D) point cloud. A 3D point cloud comprising a plurality of 3D points and a reference plane in spatial relation to the 3D point cloud is received. A 2D grid of bins is configured along the reference plane, wherein each bin of the 2D grid comprises a length and width that extends along the reference plane. For each bin of the 2D grid, a number of 3D points in the bin and a height of the bin from the reference plane is determined. An estimated volume of an object captured by the 3D point cloud based on the calculated number of 3D points in each bin and the height of each bin.