  The superior performances of pre-trained foundation models in various visual
tasks underscore their potential to enhance the 2D models' open-vocabulary
ability. Existing methods explore analogous applications in the 3D space.
However, most of them only center around knowledge extraction from singular
foundation models, which limits the open-vocabulary ability of 3D models. We
hypothesize that leveraging complementary pre-trained knowledge from various
foundation models can improve knowledge transfer from 2D pre-trained visual
language models to the 3D space. In this work, we propose FM-OV3D, a method of
Foundation Model-based Cross-modal Knowledge Blending for Open-Vocabulary 3D
Detection, which improves the open-vocabulary localization and recognition
abilities of 3D model by blending knowledge from multiple pre-trained
foundation models, achieving true open-vocabulary without facing constraints
from original 3D datasets. Specifically, to learn the open-vocabulary 3D
localization ability, we adopt the open-vocabulary localization knowledge of
the Grounded-Segment-Anything model. For open-vocabulary 3D recognition
ability, We leverage the knowledge of generative foundation models, including
GPT-3 and Stable Diffusion models, and cross-modal discriminative models like
CLIP. The experimental results on two popular benchmarks for open-vocabulary 3D
object detection show that our model efficiently learns knowledge from multiple
foundation models to enhance the open-vocabulary ability of the 3D model and
successfully achieves state-of-the-art performance in open-vocabulary 3D object
detection tasks. Code is released at
https://github.com/dmzhang0425/FM-OV3D.git.
