3D Object Counting Using Point Cloud and Neural Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for counting and arranging three-dimensional objects in retail and warehouse environments are manual, costly, and prone to inaccuracies, with limitations in accurately assessing depth and distinguishing between similar-shaped objects.
Innovation Solution
A system and method that combines 2D image recognition with 3D point cloud scanning, using a novel monocular architecture with artificial neural networks to classify and count objects, thereby overcoming the limitations of existing approaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual auditing methods are used to count and track products, then detailed inventory information can be obtained, but the process is time-consuming, expensive, and prone to human error
Solution Approach 1:
The patent replaces manual mechanical counting processes with an automated computer vision system that uses image capture devices, point cloud generation, and neural network-based object detection to automatically identify and count products on shelves, eliminating human labor while maintaining or improving accuracy
Solution Approach 2:
The patent creates a digital 3D point cloud copy of the physical shelf space to enable virtual analysis and counting of objects, allowing inventory assessment without physical manual counting by workers
2Ease of operation
If 2D image recognition is used to identify products, then object detection can be performed, but depth perception and accurate 3D bounding box estimation are limited
Solution Approach 1:
The patent transitions from 2D image analysis to 3D point cloud analysis by generating depth information through structured light scanning or LiDAR, enabling accurate 3D object detection and bounding box estimation that overcomes the inherent limitations of 2D perspective and depth ambiguity
Solution Approach 2:
The patent merges 2D image recognition capabilities with 3D point cloud data to create a hybrid system that leverages the strengths of both approaches: 2D images provide texture and color information while point clouds provide accurate depth and spatial structure
3Reliability
If traditional 3D object detectors are used, then object location can be predicted, but the system requires thousands of manually labeled 3D scenes which is time-consuming and expensive
Solution Approach 1:
The patent performs preliminary action by automatically generating 3D point cloud data and object annotations during the normal operation of the system, eliminating the need for separate time-consuming manual labeling processes. The system captures images and generates point clouds on-the-fly, using the captured data itself for training purposes
Solution Approach 2:
The system enables self-service by using its own captured images and generated point clouds to automatically create training datasets, eliminating dependence on external manual annotation services and allowing continuous self-improvement of the detection model
4Productivity
If automated systems are deployed for inventory management, then efficiency and accuracy improve, but the initial cost and system complexity increase
Solution Approach 1:
The patent designs a universal system that performs multiple functions: image capture, point cloud generation, object detection, counting, and inventory analysis, all within a single integrated platform. This multi-functionality reduces the need for separate specialized systems and justifies the initial investment through versatile application
Data Source
AI summary
Systems and methods for three-dimensional object counting in a three-dimensional space include an image capture device configured to obtain at least one 2D image of the three-dimensional space, a scanner configured to obtain a 3D point cloud of the three-dimensional space, and a processor configured to cooperate with at least one artificial neural network, the at least one artificial neural network configured to classify and count objects in the three-dimensional space.