SpatialScout: Can VLM Agents Actively Explore and Map 3D Scenes?
We introduce SpatialScout, a benchmark for evaluating vision-language model (VLM) agents on active object-level 3D mapping.
ProofPaper ↗
Key points
- Spatial intelligence requires agents to actively acquire visual evidence and integrate observations into a coherent representation of the three-dimensional world.
- Yet spatial question answering offers limited evidence of whether models can construct accurate metric maps through autonomous exploration.
- Evaluation jointly measures localization accuracy and target coverage through object-scale overlap scores that penalize missing predictions.
- SpatialScout provides a quantitative testbed for developing agents that build reliable 3D spatial representations through active perception.
Sources (2)
- [1]SpatialScout: Can VLM Agents Actively Explore and Map 3D Scenes?arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 04:17 PM
We introduce SpatialScout, a benchmark for evaluating vision-language model (VLM) agents on active object-level 3D mapping.
Spatial intelligence requires agents to actively acquire visual evidence and integrate observations into a coherent representation of the three-dimensional world.
- [2]SpatialScout: Can VLM Agents Actively Explore and Map 3D Scenes?Hugging Face Daily Papers · Oct 19, 12:00 AM · same content
Extractive summary: sentences quoted from the sources.
Before this
- Oct 18, 2026Toward Agentic Retrieval-Augmented Generation for Enterprise Retail Workflows over Heterogeneous Data Sources
- Oct 18, 2026Google Open-Sources Harness2, a Recursive Agent Harnessing Framework
- Oct 18, 2026We all joke about our rigs being space heaters, so I had Qwen vibecode it
- Oct 18, 2026Think Visually Across Space and Time: Grounded Reasoning for Video Segmentation
- Oct 17, 2026HSPR: Recovering Software-Agent Benchmark Scores via Hierarchical Posterior Inference
- Oct 16, 2026BeJEPA: A Goal-Conditioned Humanoid Behavior World Model via Unsupervised RL