UniGS: Unified Language-Image-3D Pretraining with Gaussian Splatting

• URL: http://arxiv.org/abs/2502.17860v2
• Date: Thu, 27 Feb 2025 08:30:00 GMT
• Title: UniGS: Unified Language-Image-3D Pretraining with Gaussian Splatting
• Authors: Haoyuan Li, Yanpeng Zhou, Tao Tang, Jifei Song, Yihan Zeng, Michael Kampffmeyer, Hang Xu, Xiaodan Liang,
• Abstract summary: We propose UniGS, integrating 3D Gaussian Splatting (3DGS) into multi-modal pre-training to enhance the 3D representation.<n>We demonstrate the effectiveness of UniGS in learning a more general and stronger aligned multi-modal representation.
• Score: 68.37013525040891
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent advancements in multi-modal 3D pre-training methods have shown promising efficacy in learning joint representations of text, images, and point clouds. However, adopting point clouds as 3D representation fails to fully capture the intricacies of the 3D world and exhibits a noticeable gap between the discrete points and the dense 2D pixels of images. To tackle this issue, we propose UniGS, integrating 3D Gaussian Splatting (3DGS) into multi-modal pre-training to enhance the 3D representation. We first rely on the 3DGS representation to model the 3D world as a collection of 3D Gaussians with color and opacity, incorporating all the information of the 3D scene while establishing a strong connection with 2D images. Then, to achieve Language-Image-3D pertaining, UniGS starts with a pre-trained vision-language model to establish a shared visual and textual space through extensive real-world image-text pairs. Subsequently, UniGS employs a 3D encoder to align the optimized 3DGS with the Language-Image representations to learn unified multi-modal representations. To facilitate the extraction of global explicit 3D features by the 3D encoder and achieve better cross-modal alignment, we additionally introduce a novel Gaussian-Aware Guidance module that guides the learning of fine-grained representations of the 3D domain. Through extensive experiments across the Objaverse, ABO, MVImgNet and SUN RGBD datasets with zero-shot classification, text-driven retrieval and open-world understanding tasks, we demonstrate the effectiveness of UniGS in learning a more general and stronger aligned multi-modal representation. Specifically, UniGS achieves leading results across different 3D tasks with remarkable improvements over previous SOTA, Uni3D, including on zero-shot classification (+9.36%), text-driven retrieval (+4.3%) and open-world understanding (+7.92%).

Related papers

• Unifying 2D and 3D Vision-Language Understanding [85.84054120018625]
  We introduce UniVLG, a unified architecture for 2D and 3D vision-language learning. UniVLG bridges the gap between existing 2D-centric models and the rich 3D sensory data available in embodied systems.
  arXiv  Detail & Related papers  (2025-03-13T17:56:22Z)
• CLIP-GS: Unifying Vision-Language Representation with 3D Gaussian Splatting [88.24743308058441]
  We present CLIP-GS, a novel multimodal representation learning framework grounded in 3DGS.<n>We develop an efficient way to generate triplets of 3DGS, images, and text, facilitating CLIP-GS in learning unified multimodal representations.
  arXiv  Detail & Related papers  (2024-12-26T09:54:25Z)
• GSemSplat: Generalizable Semantic 3D Gaussian Splatting from Uncalibrated Image Pairs [33.74118487769923]
  We introduce GSemSplat, a framework that learns semantic representations linked to 3D Gaussians without per-scene optimization, dense image collections or calibration.<n>We employ a dual-feature approach that leverages both region-specific and context-aware semantic features as supervision in the 2D space.
  arXiv  Detail & Related papers  (2024-12-22T09:06:58Z)
• 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)
• 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)
• Uni3D: Exploring Unified 3D Representation at Scale [66.26710717073372]
  We present Uni3D, a 3D foundation model to explore the unified 3D representation at scale. Uni3D uses a 2D ViT end-to-end pretrained to align the 3D point cloud features with the image-text aligned features. We show that the strong Uni3D representation also enables applications such as 3D painting and retrieval in the wild.
  arXiv  Detail & Related papers  (2023-10-10T16:49:21Z)

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.