Gaussian2Scene: 3D Scene Representation Learning via Self-supervised Learning with 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2506.08777v2
• Date: Wed, 11 Jun 2025 08:42:02 GMT
• Title: Gaussian2Scene: 3D Scene Representation Learning via Self-supervised Learning with 3D Gaussian Splatting
• Authors: Keyi Liu, Weidong Yang, Ben Fei, Ying He,
• Abstract summary: Self-supervised learning (SSL) for point cloud pre-training has become a cornerstone for many 3D vision tasks.<n>We propose a novel scene-level SSL framework that leverages the efficiency and explicit nature of 3D Gaussian Splatting (3DGS) for pre-training.
• Score: 6.678115792482272
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-supervised learning (SSL) for point cloud pre-training has become a cornerstone for many 3D vision tasks, enabling effective learning from large-scale unannotated data. At the scene level, existing SSL methods often incorporate volume rendering into the pre-training framework, using RGB-D images as reconstruction signals to facilitate cross-modal learning. This strategy promotes alignment between 2D and 3D modalities and enables the model to benefit from rich visual cues in the RGB-D inputs. However, these approaches are limited by their reliance on implicit scene representations and high memory demands. Furthermore, since their reconstruction objectives are applied only in 2D space, they often fail to capture underlying 3D geometric structures. To address these challenges, we propose Gaussian2Scene, a novel scene-level SSL framework that leverages the efficiency and explicit nature of 3D Gaussian Splatting (3DGS) for pre-training. The use of 3DGS not only alleviates the computational burden associated with volume rendering but also supports direct 3D scene reconstruction, thereby enhancing the geometric understanding of the backbone network. Our approach follows a progressive two-stage training strategy. In the first stage, a dual-branch masked autoencoder learns both 2D and 3D scene representations. In the second stage, we initialize training with reconstructed point clouds and further supervise learning using the geometric locations of Gaussian primitives and rendered RGB images. This process reinforces both geometric and cross-modal learning. We demonstrate the effectiveness of Gaussian2Scene across several downstream 3D object detection tasks, showing consistent improvements over existing pre-training methods.

Related papers

• UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting [64.31900521467362]
  No existing pre-training method is equally effective for both object- and scene-level point clouds.<n>We introduce UniPre3D, the first unified pre-training method that can be seamlessly applied to point clouds of any scale and 3D models of any architecture.
  arXiv  Detail & Related papers  (2025-06-11T17:23:21Z)
• EG-Gaussian: Epipolar Geometry and Graph Network Enhanced 3D Gaussian Splatting [9.94641948288285]
  EG-Gaussian utilizes epipolar geometry and graph networks for 3D scene reconstruction.<n>Our approach significantly improves reconstruction accuracy compared to 3DGS-based methods.
  arXiv  Detail & Related papers  (2025-04-18T08:10:39Z)
• OVGaussian: Generalizable 3D Gaussian Segmentation with Open Vocabularies [112.80292725951921]
  textbfOVGaussian is a generalizable textbfOpen-textbfVocabulary 3D semantic segmentation framework based on the 3D textbfGaussian representation.<n>We first construct a large-scale 3D scene dataset based on 3DGS, dubbed textbfSegGaussian, which provides detailed semantic and instance annotations for both Gaussian points and multi-view images.<n>To promote semantic generalization across scenes, we introduce Generalizable Semantic Rasterization (GSR), which leverages a
  arXiv  Detail & Related papers  (2024-12-31T07:55:35Z)
• Unified Scene Representation and Reconstruction for 3D Large Language Models [40.693839066536505]
  Existing approaches extract point clouds either from ground truth (GT) geometry or 3D scenes reconstructed by auxiliary models. We introduce Uni3DR2 extracts 3D geometric and semantic aware representation features via the frozen 2D foundation models. Our learned 3D representations not only contribute to the reconstruction process but also provide valuable knowledge for LLMs.
  arXiv  Detail & Related papers  (2024-04-19T17:58:04Z)
• 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)
• 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)
• PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm [111.16358607889609]
  We introduce a novel universal 3D pre-training framework designed to facilitate the acquisition of efficient 3D representation.<n>For the first time, PonderV2 achieves state-of-the-art performance on 11 indoor and outdoor benchmarks, implying its effectiveness.
  arXiv  Detail & Related papers  (2023-10-12T17:59:57Z)
• SSR-2D: Semantic 3D Scene Reconstruction from 2D Images [54.46126685716471]
  In this work, we explore a central 3D scene modeling task, namely, semantic scene reconstruction without using any 3D annotations. The key idea of our approach is to design a trainable model that employs both incomplete 3D reconstructions and their corresponding source RGB-D images. Our method achieves the state-of-the-art performance of semantic scene completion on two large-scale benchmark datasets MatterPort3D and ScanNet.
  arXiv  Detail & Related papers  (2023-02-07T17:47:52Z)
• Pri3D: Can 3D Priors Help 2D Representation Learning? [37.35721274841419]
  We introduce an approach to learn view-invariant,geometry-aware representations for network pre-training. We employ contrastive learning under both multi-view im-age constraints and image-geometry constraints to encode3D priors into learned 2D representations.
  arXiv  Detail & Related papers  (2021-04-22T17:59:30Z)
• 3D-to-2D Distillation for Indoor Scene Parsing [78.36781565047656]
  We present a new approach that enables us to leverage 3D features extracted from large-scale 3D data repository to enhance 2D features extracted from RGB images. First, we distill 3D knowledge from a pretrained 3D network to supervise a 2D network to learn simulated 3D features from 2D features during the training. Second, we design a two-stage dimension normalization scheme to calibrate the 2D and 3D features for better integration. Third, we design a semantic-aware adversarial training model to extend our framework for training with unpaired 3D data.
  arXiv  Detail & Related papers  (2021-04-06T02:22:24Z)

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.