FMGS: Foundation Model Embedded 3D Gaussian Splatting for Holistic 3D
  Scene Understanding
Xingxing Zuo Pouya Samangouei Yunwen Zhou Yan Di Mingyang Li
Precisely perceiving the geometric and semantic properties of real-world 3D
objects is crucial for the continued evolution of augmented reality and robotic
applications. To this end, we present \algfull{} (\algname{}), which
incorporates vision-language embeddings of foundation models into 3D Gaussian
Splatting (GS). The key contribution of this work is an efficient method to
reconstruct and represent 3D vision-language models. This is achieved by
distilling feature maps generated from image-based foundation models into those
rendered from our 3D model. To ensure high-quality rendering and fast training,
we introduce a novel scene representation by integrating strengths from both GS
and multi-resolution hash encodings (MHE). Our effective training procedure
also introduces a pixel alignment loss that makes the rendered feature distance
of same semantic entities close, following the pixel-level semantic boundaries.
Our results demonstrate remarkable multi-view semantic consistency,
facilitating diverse downstream tasks, beating state-of-the-art methods by
$\mathbf{10.2}$ percent on open-vocabulary language-based object detection,
despite that we are $\mathbf{851\times}$ faster for inference. This research
explores the intersection of vision, language, and 3D scene representation,
paving the way for enhanced scene understanding in uncontrolled real-world
environments. We plan to release the code upon paper acceptance.