GS-CLIP: Gaussian Splatting for Contrastive Language-Image-3D Pretraining from Real-World Data

• URL: http://arxiv.org/abs/2402.06198v2
• Date: Tue, 13 Feb 2024 15:33:41 GMT
• Title: GS-CLIP: Gaussian Splatting for Contrastive Language-Image-3D Pretraining from Real-World Data
• Authors: Haoyuan Li, Yanpeng Zhou, Yihan Zeng, Hang Xu, Xiaodan Liang
• Abstract summary: 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.
• Score: 73.06536202251915
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• GSD: View-Guided Gaussian Splatting Diffusion for 3D Reconstruction [52.04103235260539]
  We present a diffusion model approach based on Gaussian Splatting representation for 3D object reconstruction from a single view. The model learns to generate 3D objects represented by sets of GS ellipsoids. The final reconstructed objects explicitly come with high-quality 3D structure and texture, and can be efficiently rendered in arbitrary views.
  arXiv  Detail & Related papers  (2024-07-05T03:43:08Z)
• DreamScape: 3D Scene Creation via Gaussian Splatting joint Correlation Modeling [23.06464506261766]
  We present DreamScape, a method for creating highly consistent 3D scenes solely from textual descriptions. Our approach involves a 3D Gaussian Guide for scene representation, consisting of semantic primitives (objects) and their spatial transformations. A progressive scale control is tailored during local object generation, ensuring that objects of different sizes and densities adapt to the scene.
  arXiv  Detail & Related papers  (2024-04-14T12:13:07Z)
• Semantic Gaussians: Open-Vocabulary Scene Understanding with 3D Gaussian Splatting [27.974762304763694]
  We introduce Semantic Gaussians, a novel open-vocabulary scene understanding approach based on 3D Gaussian Splatting. Unlike existing methods, we design a versatile projection approach that maps various 2D semantic features into a novel semantic component of 3D Gaussians. We build a 3D semantic network that directly predicts the semantic component from raw 3D Gaussians for fast inference.
  arXiv  Detail & Related papers  (2024-03-22T21:28:19Z)
• Sculpting Holistic 3D Representation in Contrastive Language-Image-3D Pre-training [51.632418297156605]
  We introduce MixCon3D, a method aiming to sculpt holistic 3D representation in contrastive language-image-3D pre-training. We develop the 3D object-level representation from complementary perspectives, e.g., multi-view rendered images with the point cloud. Then, MixCon3D performs language-3D contrastive learning, comprehensively depicting real-world 3D objects and bolstering text alignment.
  arXiv  Detail & Related papers  (2023-11-03T06:05:36Z)
• VL-SAT: Visual-Linguistic Semantics Assisted Training for 3D Semantic Scene Graph Prediction in Point Cloud [51.063494002003154]
  3D semantic scene graph (3DSSG) prediction in the point cloud is challenging since the 3D point cloud only captures geometric structures with limited semantics compared to 2D images. We propose Visual-Linguistic Semantics Assisted Training scheme that can significantly empower 3DSSG prediction models with discrimination about long-tailed and ambiguous semantic relations.
  arXiv  Detail & Related papers  (2023-03-25T09:14:18Z)
• CLIP$^2$: Contrastive Language-Image-Point Pretraining from Real-World Point Cloud Data [80.42480679542697]
  We propose Contrastive Language-Image-Point Cloud Pretraining (CLIP$2$) to learn the transferable 3D point cloud representation in realistic scenarios. Specifically, we exploit naturally-existed correspondences in 2D and 3D scenarios, and build well-aligned and instance-based text-image-point proxies from those complex scenarios.
  arXiv  Detail & Related papers  (2023-03-22T09:32:45Z)
• 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)

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.