Kitti C: Monocular 3D Object Detection Data for Test-Time Adaptation

MonoTTA created this dataset for research on fully test-time adaptation in monocular 3D object detection, based on the KITTI benchmark. The dataset is hosted on Hugging Face by anthemlin and was last updated on July 9, 2025. Its specific contents are derived from the methods described in the associated arXiv paper and GitHub repository.

Use Cases

• Benchmarking monocular 3D object detection models based on the KITTI framework.
• Researching test-time adaptation algorithms for autonomous driving perception.
• Training or fine-tuning vision models for 3D bounding box prediction from single images.

Strengths

• Dataset is directly linked to a peer-reviewed arXiv paper (2405.19682v1) detailing its creation method.
• Associated open-source code is available on GitHub for reproducibility and verification.
• Last updated on 2025-07-09, indicating recent maintenance.

Limitations

• Description metadata is limited; actual data quality, size, and format require manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred from the source paper or code.
• Row count and file size are unknown, which may limit suitability assessment for large-scale projects.

Provenance

Source

Based on the KITTI dataset, created by the MonoTTA research project.

Collection Method

Created for research on fully test-time adaptation for monocular 3D object detection.

Time Range

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Freshness

Last updated 2025-07-09 12:51:14

Geography

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