Papers › MiKASA: Multi-Key-Anchor & Scene-Aware Transformer for 3D Visual Grounding

MiKASA: Multi-Key-Anchor & Scene-Aware Transformer for 3D Visual Grounding

5 Mar 2024CVPR 2024 1arXiv:2403.03077archive 2025-07-28

Chun-Peng Chang, Shaoxiang Wang, Alain Pagani, Didier Stricker

3D visual grounding involves matching natural language descriptions with their corresponding objects in 3D spaces. Existing methods often face challenges with accuracy in object recognition and struggle in interpreting complex linguistic queries, particularly with descriptions that involve multiple anchors or are view-dependent. In response, we present the MiKASA (Multi-Key-Anchor Scene-Aware) Transformer. Our novel end-to-end trained model integrates a self-attention-based scene-aware object encoder and an original multi-key-anchor technique, enhancing object recognition accuracy and the understanding of spatial relationships. Furthermore, MiKASA improves the explainability of decision-making, facilitating error diagnosis. Our model achieves the highest overall accuracy in the Referit3D challenge for both the Sr3D and Nr3D datasets, particularly excelling by a large margin in categories that require viewpoint-dependent descriptions.

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Tasks

3D visual groundingDecision MakingObjectObject RecognitionVisual Grounding

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Methods

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

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