3D scene graph generation (3DSGG), which involves classifying objects and predicates, is an emerging topic in 3D scene understanding. Recent studies leveraging graph neural networks (GNNs) have introduced sophisticated architectures that enhance classification performance. However, since GNNs serve as the core and constitute the majority of parameters in 3DSGG models, their computational demands substantially increase overall complexity, which makes it difficult to determine the optimal model capacity. In this paper, we propose the first compression framework for lightweight 3DSGG models, based on pruning-as-search and knowledge distillation. This framework integrates multiple strategies and modules. In phase 1, the framework identifies the optimal compression ratio through pruning-as-search. In phase 2, to mitigate the accuracy loss incurred during compression, we employ structured pruning and a novel knowledge distillation strategy that effectively transfers precise information from the teacher to the compressed model. Experimental results show that our approach reduces model size by more than half while improving classification accuracy. Code is available athttps://github.com/hojunking/3DSGG-compression.

LiDAR-based 3D object detection has been widely utilized in fields such as autonomous driving and robotics. However, the black-box nature of 3D models limits their interpretability, making it difficult to understand their predictions and evaluate significant feature contributions, which are essential for safety-critical applications. To address this, we propose a novel knowledge distillation method for 3D object detection that integrates explanation-based and aggregation techniques to achieve effective knowledge transfer and, as a result, enhance the model’s interpretability. Our method generates attribution maps that highlight the importance of 3D points in the teacher model and aggregates them into a single map. This map is aligned with the student model’s pillar features using corresponding coordinates, allowing pillar-wise feature mapping. Building on this feature mapping, to the best of our knowledge, this is the first study to propose a distillation process that effectively transfers the teacher model’s explanations of critical regions to the student model. Experimental results demonstrate that the proposed method increases 3D and BEV mAP by up to 2.09% and 0.84%, respectively, compared to the existing models.