PinpointQA: A Benchmark for Small Object-Centric Spatial Understanding in Indoor Videos
Reliable embodied interaction in indoor environments requires agents to precisely localize small everyday objects from visual observations. Yet this fundamental capability remains challenging for multimodal large language models (MLLMs), particularly when spatial understanding must be performed from indoor videos. Existing benchmarks study video spatial intelligence and embodied reasoning, but do not directly evaluate whether a model can localize a small target object and express its position with sufficient precision. We introduce PinpointQA, a benchmark built from ScanNet++ and ScanNet200. It contains 1,024 scenes and 10,094 QA pairs across four progressively challenging tasks: Target P...
| Authors | Zhiyu Zhou, Peilin Liu, Ruoxuan Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 10 April 2026 |
| Last updated at source | 11 August 2026 |
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