  Foundation models have achieved remarkable results in 2D and language tasks
like image segmentation, object detection, and visual-language understanding.
However, their potential to enrich 3D scene representation learning is largely
untapped due to the existence of the domain gap. In this work, we propose an
innovative methodology called Bridge3D to address this gap by pre-training 3D
models using features, semantic masks, and captions sourced from foundation
models. Specifically, our method employs semantic masks from foundation models
to guide the masking and reconstruction process for the masked autoencoder,
enabling more focused attention on foreground representations. Moreover, we
bridge the 3D-text gap at the scene level using image captioning foundation
models, thereby facilitating scene-level knowledge distillation. We further
extend this bridging effort by introducing an innovative object-level knowledge
distillation method that harnesses highly accurate object-level masks and
semantic text data from foundation models. Our methodology significantly
surpasses the performance of existing state-of-the-art methods in 3D object
detection and semantic segmentation tasks. For instance, on the ScanNet
dataset, Bridge3D improves the baseline by a notable margin of 6.3%. Code will
be available at: https://github.com/Zhimin-C/Bridge3D
