What is it about?

The paper introduces a new method for efficiently packing 3D objects into a specific container, much like solving a 3D jigsaw puzzle. This process is essential for industries like packaging, transportation, and 3D printing. Traditional methods often struggle with fitting objects perfectly, leading to wasted space or objects that get interlocked. The authors present a new technique called "Scalable Spectral Packing" that uses Fast Fourier Transforms to find the best way to fit these objects together without overlapping and interlocking.

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Why is it important?

Packing generic 3D objects into a container is a common task in many industries, including packaging, transportation, and manufacturing. It is a difficult problem due to challenging geometric constrains and a large search space. Most previous works do not scale and can only handle simple objects.​ In this work, the authors propose a novel algorithm based on voxelization and Fast Fourier Transforms. The resulting algorithm is scalable, produces dense and interlocking-free packings. It produces state-of-the-art performance on the benchmark when compared to existing methods in both density and speed.


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Qiaodong Cui

Read the Original

This page is a summary of: Dense, Interlocking-Free and Scalable Spectral Packing of Generic 3D Objects, ACM Transactions on Graphics, July 2023, ACM (Association for Computing Machinery),
DOI: 10.1145/3592126.
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