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About this paper
Ankit Goyal, Kaiyu Yang, Dawei Yang, Jia Deng
Rel3D introduces the first large-scale, human-annotated 3D dataset for grounding spatial relations, employing a minimally contrastive data collection method to reduce dataset bias. It quantitatively shows the high predictive power of 3D information for spatial relations and reveals limitations of current 2D visual relationship models on unbiased 3D tasks.
Key insights
- The minimally contrastive data collection method effectively mitigates language and 2D spatial biases, forcing models to learn genuine spatial reasoning based on 3D configurations.
- Accurate 3D object properties (position, scale, pose, and intrinsic orientation) are highly predictive of spatial relations, underscoring their critical role in scene understanding.
- Current state-of-the-art 2D visual relationship detection models over-rely on dataset biases and struggle when evaluated on unbiased 3D spatial relation tasks.
Summary by alphaXiv.
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