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Description
A dataset for metric scale monocular geometry estimation, addressing limitations in current foundation models. The dataset was created by authors Yuanbo Xiangli, Hanyu Chen, Xueqing Tsang, and Noah Snavely, with a project page available. The dataset listing was last updated on 2026.06.01.
Use Cases
Training models for metric depth estimation based on the dataset's in-the-wild, metrically-grounded nature.
Benchmarking monocular 3D reconstruction algorithms based on the dataset's focus on metric scale geometry.
Improving generalization of scene geometry models based on the dataset's aim to address persistent model limitations.
Strengths
Dataset is explicitly described as 'metrically-grounded', providing a key property for geometry tasks.
Dataset is described as 'in-the-wild', suggesting real-world applicability.
Associated with a published paper and a dedicated project page, indicating academic rigor.
Limitations
Column-level documentation is absent; field semantics must be inferred after download.
Row count is unknown, which may limit suitability assessment.
Description metadata is limited; actual data quality requires manual inspection after download.
Provenance
Source
huggingface, uploaded by user yx642.
Collection Method
Likely aggregated or created for research on monocular geometry estimation, as described in the associated paper.
Time Range
null
Freshness
Last updated 2026-06-01 22:06:02; freshness should be verified.
Geography
null
License is unknown; users must verify permissions before use.