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About this paper
Austin T. Wang, ZeMing Gong, Angel X. Chang
An independent research group from Simon Fraser University and Amii introduces ViGiL3D, a diagnostic dataset and linguistic analysis framework for 3D visual grounding. This work identifies critical linguistic diversity gaps in existing datasets and demonstrates a significant performance drop for state-of-the-art models when confronted with more challenging, diverse language patterns.
Key insights
- Existing 3DVG datasets, whether manually curated or LLM-scaled, demonstrate significant linguistic homogeneity, often lacking negation, diverse relationship types, and varied sentence structures.
- Current 3DVG models, encompassing CLIP-aligned, LLM-based, and transformer architectures, consistently show a substantial performance degradation when evaluated on linguistically diverse prompts from ViGiL3D compared to standard benchmarks.
- The quality and diversity of linguistic patterns in training data are more crucial than raw data volume for developing robust and generalizable 3DVG models capable of genuine language understanding.
Summary by alphaXiv.
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