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About this paper
Ayaka Nanri, Klara Reichard, Mert Kiray, Federico Tombari, Benjamin Busam, Asako Kanezaki
Researchers from Institute of Science Tokyo, Technical University of Munich, BMW Group, Munich Center for Machine Learning (MCML), Obsphera, Google, Tohoku University, and RIKEN introduced a viewpoint-aware 3D referring segmentation benchmark and a viewpoint encoder to enable 3D large multimodal models to understand observer-centric spatial relations. Their method improved segmentation accuracy (mIoU) on viewpoint-dependent instructions from a baseline of 0.0737 to 0.4737 for 3D-LLaVA.
Key insights
- Explicitly modeling the observer's viewpoint is crucial for 3D LMMs to accurately interpret observer-centric spatial relations in 3D scenes.
- Existing 3D LMMs, without explicit viewpoint conditioning, perform poorly on tasks requiring viewpoint-dependent spatial reasoning, indicating a significant gap in their understanding.
- Integrating viewpoint information as a dedicated token via a viewpoint encoder is more effective than providing it as raw text in the prompt or fine-tuning without explicit viewpoint input.
Summary by alphaXiv.
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