Language Conditioned Spatial Relation Reasoning for 3D Object Grounding
This addresses the challenge of disambiguating spatial relations for 3D object grounding, which is crucial for applications like robotics and augmented reality, representing a strong specific gain in this domain.
The paper tackles the problem of localizing objects in 3D scenes based on natural language by proposing a language-conditioned transformer model with a spatial self-attention layer, achieving significant outperformance over state-of-the-art on datasets like Nr3D, Sr3D, and ScanRefer.
Localizing objects in 3D scenes based on natural language requires understanding and reasoning about spatial relations. In particular, it is often crucial to distinguish similar objects referred by the text, such as "the left most chair" and "a chair next to the window". In this work we propose a language-conditioned transformer model for grounding 3D objects and their spatial relations. To this end, we design a spatial self-attention layer that accounts for relative distances and orientations between objects in input 3D point clouds. Training such a layer with visual and language inputs enables to disambiguate spatial relations and to localize objects referred by the text. To facilitate the cross-modal learning of relations, we further propose a teacher-student approach where the teacher model is first trained using ground-truth object labels, and then helps to train a student model using point cloud inputs. We perform ablation studies showing advantages of our approach. We also demonstrate our model to significantly outperform the state of the art on the challenging Nr3D, Sr3D and ScanRefer 3D object grounding datasets.