  3D Shape represented as point cloud has achieve advancements in multimodal
pre-training to align image and language descriptions, which is curial to
object identification, classification, and retrieval. However, the discrete
representations of point cloud lost the object's surface shape information and
creates a gap between rendering results and 2D correspondences. To address this
problem, we propose GS-CLIP for the first attempt to introduce 3DGS (3D
Gaussian Splatting) into multimodal pre-training to enhance 3D representation.
GS-CLIP leverages a pre-trained vision-language model for a learned common
visual and textual space on massive real world image-text pairs and then learns
a 3D Encoder for aligning 3DGS optimized per object. Additionally, a novel
Gaussian-Aware Fusion is proposed to extract and fuse global explicit feature.
As a general framework for language-image-3D pre-training, GS-CLIP is agnostic
to 3D backbone networks. Experiments on challenging shows that GS-CLIP
significantly improves the state-of-the-art, outperforming the previously best
results.
