CLIP-GS: Unifying Vision-Language Representation with 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2412.19142v1
• Date: Thu, 26 Dec 2024 09:54:25 GMT
• Title: CLIP-GS: Unifying Vision-Language Representation with 3D Gaussian Splatting
• Authors: Siyu Jiao, Haoye Dong, Yuyang Yin, Zequn Jie, Yinlong Qian, Yao Zhao, Humphrey Shi, Yunchao Wei,
• Abstract summary: We present CLIP-GS, a novel multimodal representation learning framework grounded in 3DGS. We develop an efficient way to generate triplets of 3DGS, images, and text, facilitating CLIP-GS in learning unified multimodal representations.
• Score: 88.24743308058441
• License:
• Abstract: Recent works in 3D multimodal learning have made remarkable progress. However, typically 3D multimodal models are only capable of handling point clouds. Compared to the emerging 3D representation technique, 3D Gaussian Splatting (3DGS), the spatially sparse point cloud cannot depict the texture information of 3D objects, resulting in inferior reconstruction capabilities. This limitation constrains the potential of point cloud-based 3D multimodal representation learning. In this paper, we present CLIP-GS, a novel multimodal representation learning framework grounded in 3DGS. We introduce the GS Tokenizer to generate serialized gaussian tokens, which are then processed through transformer layers pre-initialized with weights from point cloud models, resulting in the 3DGS embeddings. CLIP-GS leverages contrastive loss between 3DGS and the visual-text embeddings of CLIP, and we introduce an image voting loss to guide the directionality and convergence of gradient optimization. Furthermore, we develop an efficient way to generate triplets of 3DGS, images, and text, facilitating CLIP-GS in learning unified multimodal representations. Leveraging the well-aligned multimodal representations, CLIP-GS demonstrates versatility and outperforms point cloud-based models on various 3D tasks, including multimodal retrieval, zero-shot, and few-shot classification.

Related papers

• Hyperbolic Contrastive Learning for Hierarchical 3D Point Cloud Embedding [21.50985015159827]
  We extend the 3D Point Cloud modality in hyperbolic multi-modal contrastive pre-training. We also explore the entailment, modality gap, and alignment regularizers for learning hierarchical 3D embeddings.
  arXiv  Detail & Related papers  (2025-01-04T13:27:18Z)
• GS-PT: Exploiting 3D Gaussian Splatting for Comprehensive Point Cloud Understanding via Self-supervised Learning [15.559369116540097]
  Self-supervised learning of point cloud aims to leverage unlabeled 3D data to learn meaningful representations without reliance on manual annotations. We propose GS-PT, which integrates 3D Gaussian Splatting (3DGS) into point cloud self-supervised learning for the first time. Our pipeline utilizes transformers as the backbone for self-supervised pre-training and introduces novel contrastive learning tasks through 3DGS.
  arXiv  Detail & Related papers  (2024-09-08T03:46:47Z)
• WE-GS: An In-the-wild Efficient 3D Gaussian Representation for Unconstrained Photo Collections [8.261637198675151]
  Novel View Synthesis (NVS) from unconstrained photo collections is challenging in computer graphics. We propose an efficient point-based differentiable rendering framework for scene reconstruction from photo collections. Our approach outperforms existing approaches on the rendering quality of novel view and appearance synthesis with high converge and rendering speed.
  arXiv  Detail & Related papers  (2024-06-04T15:17:37Z)
• LP-3DGS: Learning to Prune 3D Gaussian Splatting [71.97762528812187]
  We propose learning-to-prune 3DGS, where a trainable binary mask is applied to the importance score that can find optimal pruning ratio automatically. Experiments have shown that LP-3DGS consistently produces a good balance that is both efficient and high quality.
  arXiv  Detail & Related papers  (2024-05-29T05:58:34Z)
• SAGS: Structure-Aware 3D Gaussian Splatting [53.6730827668389]
  We propose a structure-aware Gaussian Splatting method (SAGS) that implicitly encodes the geometry of the scene. SAGS reflects to state-of-the-art rendering performance and reduced storage requirements on benchmark novel-view synthesis datasets.
  arXiv  Detail & Related papers  (2024-04-29T23:26:30Z)
• GS-CLIP: Gaussian Splatting for Contrastive Language-Image-3D Pretraining from Real-World Data [73.06536202251915]
  3D Shape represented as point cloud has achieve advancements in multimodal pre-training to align image and language descriptions. We propose GS-CLIP for the first attempt to introduce 3DGS into multimodal pre-training to enhance 3D representation.
  arXiv  Detail & Related papers  (2024-02-09T05:46:47Z)
• ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding [110.07170245531464]
  Current 3D models are limited by datasets with a small number of annotated data and a pre-defined set of categories. Recent advances have shown that similar problems can be significantly alleviated by employing knowledge from other modalities, such as language. We learn a unified representation of images, texts, and 3D point clouds by pre-training with object triplets from the three modalities.
  arXiv  Detail & Related papers  (2022-12-10T01:34:47Z)
• PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models [56.324516906160234]
  Generalizable 3D part segmentation is important but challenging in vision and robotics. This paper explores an alternative way for low-shot part segmentation of 3D point clouds by leveraging a pretrained image-language model, GLIP. We transfer the rich knowledge from 2D to 3D through GLIP-based part detection on point cloud rendering and a novel 2D-to-3D label lifting algorithm.
  arXiv  Detail & Related papers  (2022-12-03T06:59:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.