AllenXiangX/SnowflakeNet

(TPAMI 2023) Snowflake Point Deconvolution for Point Cloud Completion and Generation with Skip-Transformer

40
/ 100
Emerging

This project helps industrial designers, architects, or engineers reconstruct complete 3D models from incomplete scans. It takes partial or noisy 3D point cloud data as input and outputs a refined, detailed, and complete 3D point cloud model. The primary users are professionals who work with 3D scanning and modeling for tasks like quality inspection, reverse engineering, or digital asset creation.

200 stars. No commits in the last 6 months.

Use this if you need to automatically fill in missing details, generate higher-resolution versions, or reconstruct full 3D shapes from sparse or partial 3D scan data.

Not ideal if your primary need is for object classification or segmentation, rather than the generation or completion of 3D geometries.

3D-scanning reverse-engineering product-design architecture-modeling quality-inspection
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 10 / 25
Maturity 16 / 25
Community 14 / 25

How are scores calculated?

Stars

200

Forks

18

Language

Python

License

MIT

Last pushed

Jul 22, 2024

Commits (30d)

0

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