Thinking Outside The Box: Computing 3D Volume in 2D
2017
- 101Usage
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage101
- Downloads81
- Abstract Views20
Artifact Description
This project explores how to compute 3D volume of cardboard boxes in 2D without a calibrated camera. Computer vision techniques to obtain 3D volume typically require camera calibration, the standard method for mapping 3D points to 2D. We created our own solution that doesn’t rely on camera calibration and obtains the areas of each box with unknown dimensions with the help of a chessboard pattern placed on each box side. The solution is a proportion that given the box area in pixels, chessboard pattern in pixels, and the chessboard pattern in inches, determines the box area in inches. We tested this method on 20 boxes, 5 pictures of each side for one box. The results showed positive feedback compared with the defined areas/ volumes and compared with the results of our homographies. Ultimately we determined that our solution has the potential, with improved photos, test methods, etc. to accurately find an unknown box’s volume given only the provided 2D data.
Bibliographic Details
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know