arXiv (All)
open access
What Makes a Good Visual Representation for Spatial Understanding in MLLMs?
Spatial understanding is a necessary step in moving multimodal large language models (MLLMs) toward reasoning about the 3D physical world. Their visual pretraining data, however, is almost entirely 2D, so the visual tokens processed by MLLMs often lack inherent 3D structural information. A growing n
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- OneCanvas: 3D Scene Understanding via Panoramic Reprojection#1118611
- Mind the Spatial Gap: A Benchmark for Geospatial Reasoning and Constrained Spatial Editing#1296888
- RoTA-Bench: Evaluating 3D Spatial Reasoning through Interactive Projection Alignment#1344696
- Plan-Bench: Can Existing VLMs Go Well Beyond Perception to Reason and Plan in Embodied Worlds?#1344716
- Is Visual Prompting All You Need? Studying VLM Spatial Reasoning under Progressive Visual Scaffolds#480099
Record · ID 1343048
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive