  The scarcity of large-scale 3D-text paired data poses a great challenge on
open vocabulary 3D scene understanding, and hence it is popular to leverage
internet-scale 2D data and transfer their open vocabulary capabilities to 3D
models through knowledge distillation. However, the existing distillation-based
3D scene understanding approaches rely on the representation capacity of 2D
models, disregarding the exploration of geometric priors and inherent
representational advantages offered by 3D data. In this paper, we propose an
effective approach, namely Geometry Guided Self-Distillation (GGSD), to learn
superior 3D representations from 2D pre-trained models. Specifically, we first
design a geometry guided distillation module to distill knowledge from 2D
models, and then leverage the 3D geometric priors to alleviate the inherent
noise in 2D models and enhance the representation learning process. Due to the
advantages of 3D representation, the performance of the distilled 3D student
model can significantly surpass that of the 2D teacher model. This motivates us
to further leverage the representation advantages of 3D data through
self-distillation. As a result, our proposed GGSD approach outperforms the
existing open vocabulary 3D scene understanding methods by a large margin, as
demonstrated by our experiments on both indoor and outdoor benchmark datasets.
