ModelNet-MPC: Dual-Modality Point Cloud Completion Dataset

ModelNet-MPC is a dataset for point cloud completion proposed by the paper 'DuInNet: Dual-Modality Feature Interaction for Point Cloud Completion'. The dataset, authored by xinpuliu, was last updated on the Hugging Face platform on March 1, 2025. It contains directories for ground truth (GT), images (Img), and partial point clouds with and without noise.

Use Cases

• Training point cloud completion models based on partial 3D object scans.
• Benchmarking dual-modality feature interaction methods using paired point cloud and image data.
• Evaluating model robustness based on noisy partial point cloud inputs.
• Researching 3D shape reconstruction for object categories like airplanes.

Strengths

• Proposed by a specific, cited research paper ('DuInNet'), providing academic context.
• Includes multiple data modalities (point clouds and images) for dual-modality research.
• Contains structured subsets for ground truth, partial, and noisy partial data, enabling controlled experiments.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count, file sizes, and total number of objects are unknown, which may limit suitability assessment.
• License information is not provided, requiring verification before commercial use.

Provenance

Source

Hugging Face dataset repository by author xinpuliu.

Collection Method

Likely created for the research paper 'DuInNet: Dual-Modality Feature Interaction for Point Cloud Completion'.

Freshness

Last updated 2025-03-01 12:05:46; freshness should be verified.