<p>Comparison between distances with shapes represented as point clouds in 2D/3D. For Grom

5.5 KB Excel file authored by Wenjun Zhao, last updated in May 2026. This dataset compares the computational costs of exact and entropic approximations for Gromov-Wasserstein and Wasserstein distances when applied to 2D/3D shapes represented as point clouds. It likely contains a structured analysis of algorithmic complexity relative to the number of points per shape (N).

Use Cases

• Benchmarking computational performance for shape-matching algorithms based on the described distance metrics.
• Informing algorithm selection for point cloud registration tasks based on the complexity analysis.
• Studying the trade-offs between accuracy and computational cost in entropic regularization of optimal transport.
• Designing experiments in computer graphics or computational biology that involve comparing 3D molecular or structural shapes.

Strengths

• Provides a direct comparison of computational costs for two foundational distance metrics in shape analysis.
• File is small (5.5 KB), ensuring quick download and easy inspection.
• Includes references to key literature (e.g., citations 16, 26, 27) for further study on complexity.

Limitations

• Row count and specific column definitions are unknown, requiring manual inspection to understand the data structure.
• The dataset's scope is limited to a theoretical complexity comparison; it does not contain actual point cloud data for application.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare, author Wenjun Zhao

Collection Method

Likely a synthesized analysis or computational experiment, method not detailed.

Time Range

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Freshness

Last updated 2026-05-07 17:44:49; freshness should be verified

Geography

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