3D Object Boundary Detection for Accurate Packaging Measurement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional techniques for estimating shipping costs and packaging materials are inefficient and inaccurate, relying on user guesswork due to the lack of readily available tools, leading to frustration and excess packaging costs.
Innovation Solution
A mobile device, such as a smartphone, uses a three-dimensional sensing device and point cloud generation module to calculate a physical object's boundary, enabling automated generation of shipping data and packaging recommendations without user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques use multiple different devices (tape measure, etc.) to determine dimensions, then measurement capability is provided, but device complexity and ease of operation deteriorate due to requiring multiple tools that may not be readily available
Solution Approach 1:
The patent combines multiple measurement functions (length, width, height detection) into a single mobile device with integrated camera and processing capabilities. The system merges the functions of what would traditionally require separate measuring tools into one unified device that can capture images and automatically compute three-dimensional dimensions through image processing algorithms.
Solution Approach 2:
The mobile device serves multiple functions: it acts as a camera for capturing object images, a computer for processing images and calculating dimensions, and a user interface for displaying results. This multi-functional approach eliminates the need for specialized measuring instruments while maintaining measurement capabilities.
2Ease of operation
If conventional techniques rely on user best guess for packaging sizing, then ease of operation is maintained, but measurement precision and loss of substance worsen due to inaccuracies leading to excess packaging
Solution Approach 1:
The system performs automatic dimension calculation and packaging recommendation without requiring user expertise or manual measurement. The mobile device captures images, the processor automatically computes three-dimensional dimensions using image analysis, and the system自行 generates packaging size recommendations, eliminating the need for user guesswork while improving accuracy.
Solution Approach 2:
The patent replaces manual mechanical measurement (using tape measures and physical estimation) with optical-based image capture and computational algorithms. The system uses camera images and mathematical processing to determine dimensions, substituting physical measurement tools and human estimation with automated visual inspection and computation.
3Loss of information
If conventional techniques use multiple devices and manual measurement, then dimension data can be obtained, but loss of time worsens due to the multi-step process requiring physical journey to stores
Solution Approach 1:
The system performs dimension measurement and packaging selection in advance, before the user needs to purchase materials. By capturing images and computing dimensions immediately, the system provides all necessary information upfront, allowing users to order packaging materials online or prepare them beforehand, eliminating the need for physical trips to stores.
Solution Approach 2:
The mobile device and processing system act as an intermediary between the user and packaging materials. Instead of directly measuring and then physically obtaining materials, the system mediates the process by capturing digital images, computing dimensions, and providing automated recommendations that can be used to order materials remotely, streamlining the entire workflow.
Data Source
Figure 1
Figure 2
Figure 3~4
AI summary
Physical object boundary detection techniques and systems are described. In one example, an augmented reality module generates three dimensional point cloud data. This data describes depths at respective points within a physical environment that includes the physical object. A physical object boundary detection module is then employed to filter the point cloud data by removing points that correspond to a ground plane. The module then performs a nearest neighbor search to locate a subset of the points within the filtered point cloud data that correspond to the physical object. Based on this subset, the module projects the subset of points onto the ground plane to generate a two-dimensional boundary. The two-dimensional boundary is then extruded based on a height determined from a point having a maximum distance from the ground plane from the filtered cloud point data.