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

Chun-Peng Chang, Shaoxiang Wang, Alain Pagani, Didier Stricker; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 14131-14140

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.