Depth Anything V2: Image Triplets for Monocular Depth Estimation

A collection of image triplets for training and evaluating monocular depth estimation models. Each row contains an original image alongside greyscale and colourised depth map variants. The dataset is associated with the Depth-Anything-V2 GitHub repository and was uploaded by author 'ssws3'.

Use Cases

• Training monocular depth estimation models based on the provided image-depth map pairs.
• Evaluating depth prediction accuracy using the greyscale and colourised depth variants.
• Benchmarking model performance against the Depth-Anything-V2 framework.
• Analyzing the visual characteristics of depth maps produced by different processing methods.

Strengths

• Provides aligned image triplets (original, greyscale depth, colourised depth) for direct model input-output comparison.
• Associated with a specific, named research project (Depth-Anything-V2), suggesting a clear application context.

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

Depth-Anything-V2 GitHub Repository

Freshness

Last updated 2026-05-11 22:24:55; freshness should be verified.