LLDDSS/Stereo_Depth

Youtube Self Depth QA

Release status: private review only. Do not make this repository public until every source video/image has a documented redistribution right and the Level-3 validator is independently completed.

Dataset summary

Youtube Self Depth QA is a visual spatial-reasoning benchmark generated from relative disparity maps. It has three task levels:

• Level 1: identify nearest/farthest marked point, optionally relative to a reference point.
• Level 2: select the correct ordering of four marked points.
• Level 3: choose the trajectory whose depth changes monotonically in the requested direction.

Each QA sample is rendered at six intervention baselines (0, 0.05, 0.10, 0.15, 0.2, 0.25). This release represents each (sample_id, baseline) pair as one row. The image column is embedded in Parquet for Dataset Viewer compatibility.

Performance across stereo baselines

The figure reports accuracy under the six stereo-baseline settings for Qwen2.5-VL-3B, Qwen2.5-VL-7B, Qwen3.5-4B, and Qwen3.5-4B-Base across VSR and the three dataset levels. Dashed horizontal lines mark chance performance for the corresponding task.

Splits

The train/test split is grouped by source video clip, rather than by individual frame. This avoids placing near-duplicate frames from a clip in both splits. The original split manifest reports 34 clips in total, 6 held-out test clips, and about a 10.8% test fraction for each level.

Fields

image, sample_id, baseline, level, task_type, question, answer, and metadata. metadata is a JSON string containing the geometric information used during generation; host-specific source paths are intentionally omitted.

Important limitations

• Depth/disparity values are relative and are not metric 3D depth.
• Labels are generated from estimated disparity and can inherit its failures, especially around occlusions, reflective surfaces, thin structures, and moving content.
• This dataset contains imagery derived from online videos. The publisher must document the source, creator, applicable license, access date, and redistribution right for every clip before releasing it publicly.
• Images may contain identifiable people, trademarks, or other sensitive visual content. Review, removal procedures, and applicable privacy requirements must be documented before public release.
• The current repository's generic verifier covers Levels 1–2 only. Level 3 requires a separate independent validator before a public benchmark claim.

Intended use

Research on visual spatial reasoning, disparity-aware QA, and evaluation of baseline-conditioned image understanding. It is not intended for safety-critical depth estimation, identity-related inference, or use as a source of metric depth ground truth.