Box Dimension Detection Using Depth Map Block Normal Vectors
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Solution Overview
Problem
Conventional methods for detecting the dimension of a box using depth maps are not precise due to noise and invalid depth values, leading to inefficiencies in determining the box's dimensions.
Innovation Solution
A method involving noise removal, coordinate transformation, block division, statistical analysis of average normal vectors, cluster classification, plane extraction, and vertex calculation from edge vectors to accurately determine the box's dimensions using depth maps generated by cameras.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a depth map is used to detect box dimension, then the detection process is simple, but the measurement precision is insufficient due to noise and invalid depth values
Solution Approach 1:
The patent divides the depth map into multiple blocks and processes each block separately to calculate average normal vectors. This segmentation approach reduces the impact of noise and invalid depth values on the overall measurement, thereby improving measurement precision while maintaining computational efficiency
Solution Approach 2:
The patent performs preliminary noise removal and validation of depth values before proceeding with dimension detection. By pre-processing the depth map to eliminate obvious noise and invalid data, the subsequent detection steps work with cleaner data, improving final measurement accuracy
2Measurement precision
If noise removal and block division are performed, then measurement precision improves, but device complexity increases
Solution Approach 1:
By dividing the depth map into blocks and processing them independently, the patent reduces computational complexity compared to processing the entire depth map at once. Each block can be processed in parallel, and the localized processing reduces the impact of noise while maintaining manageable computational load
Solution Approach 2:
The patent applies noise removal and block processing selectively to regions that contribute most to dimension detection, rather than uniformly processing all pixels. This partial action approach improves precision where needed while avoiding unnecessary computational overhead in less critical areas
Data Source
AI summary
A method for detecting dimension of box based on depth map includes: receiving a depth map generated by a camera, the depth map corresponds to pixels of an image including a box; performing a coordinate transformation to transform the depth map into camera coordinates of each of the pixels; dividing some of the pixels into plural blocks, each of blocks includes a number of the pixels adjacent to each other; statistically analyzing an average normal vector of each of the blocks according to the camera coordinates of the pixels of each of the blocks; classifying the blocks into plural clusters according to the average normal vector of each of the blocks; performing a plane extraction to obtain edge vectors according to plane formulas of the clusters; obtaining vertexes of the box according to the edge vectors; and obtaining a dimension of the box according the vertexes.