LongSplat: Online Generalizable 3D Gaussian Splatting from Long Sequence Images

• URL: http://arxiv.org/abs/2507.16144v1
• Date: Tue, 22 Jul 2025 01:43:51 GMT
• Title: LongSplat: Online Generalizable 3D Gaussian Splatting from Long Sequence Images
• Authors: Guichen Huang, Ruoyu Wang, Xiangjun Gao, Che Sun, Yuwei Wu, Shenghua Gao, Yunde Jia,
• Abstract summary: LongSplat is an online real-time 3D Gaussian reconstruction framework for long-sequence image input.<n>GIR encodes 3D Gaussian parameters into a structured, image-like 2D format.<n>LongSplat achieves state-of-the-art efficiency-quality trade-offs in real-time novel view synthesis.
• Score: 44.558724617615006
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 3D Gaussian Splatting achieves high-fidelity novel view synthesis, but its application to online long-sequence scenarios is still limited. Existing methods either rely on slow per-scene optimization or fail to provide efficient incremental updates, hindering continuous performance. In this paper, we propose LongSplat, an online real-time 3D Gaussian reconstruction framework designed for long-sequence image input. The core idea is a streaming update mechanism that incrementally integrates current-view observations while selectively compressing redundant historical Gaussians. Crucial to this mechanism is our Gaussian-Image Representation (GIR), a representation that encodes 3D Gaussian parameters into a structured, image-like 2D format. GIR simultaneously enables efficient fusion of current-view and historical Gaussians and identity-aware redundancy compression. These functions enable online reconstruction and adapt the model to long sequences without overwhelming memory or computational costs. Furthermore, we leverage an existing image compression method to guide the generation of more compact and higher-quality 3D Gaussians. Extensive evaluations demonstrate that LongSplat achieves state-of-the-art efficiency-quality trade-offs in real-time novel view synthesis, delivering real-time reconstruction while reducing Gaussian counts by 44\% compared to existing per-pixel Gaussian prediction methods.

Related papers

• StreamGS: Online Generalizable Gaussian Splatting Reconstruction for Unposed Image Streams [32.91936079359693]
  We propose StreamGS, an online generalizable 3DGS reconstruction method for unposed image streams.<n>StreamGS transforms image streams to 3D Gaussian streams by predicting and aggregating per-frame Gaussians.<n>Experiments on diverse datasets have demonstrated that StreamGS achieves quality on par with optimization-based approaches but does so 150 times faster.
  arXiv  Detail & Related papers  (2025-03-08T14:35:39Z)
• Compact 3D Gaussian Splatting for Static and Dynamic Radiance Fields [13.729716867839509]
  We propose a learnable mask strategy that significantly reduces the number of Gaussians while preserving high performance. In addition, we propose a compact but effective representation of view-dependent color by employing a grid-based neural field. Our work provides a comprehensive framework for 3D scene representation, achieving high performance, fast training, compactness, and real-time rendering.
  arXiv  Detail & Related papers  (2024-08-07T14:56:34Z)
• CoherentGS: Sparse Novel View Synthesis with Coherent 3D Gaussians [18.42203035154126]
  We introduce a structured Gaussian representation that can be controlled in 2D image space.<n>We then constraint the Gaussians, in particular their position, and prevent them from moving independently during optimization.<n>We demonstrate significant improvements compared to the state-of-the-art sparse-view NeRF-based approaches on a variety of scenes.
  arXiv  Detail & Related papers  (2024-03-28T15:27:13Z)
• latentSplat: Autoencoding Variational Gaussians for Fast Generalizable 3D Reconstruction [48.86083272054711]
  latentSplat is a method to predict semantic Gaussians in a 3D latent space that can be splatted and decoded by a light-weight generative 2D architecture. We show that latentSplat outperforms previous works in reconstruction quality and generalization, while being fast and scalable to high-resolution data.
  arXiv  Detail & Related papers  (2024-03-24T20:48:36Z)
• GVGEN: Text-to-3D Generation with Volumetric Representation [89.55687129165256]
  3D Gaussian splatting has emerged as a powerful technique for 3D reconstruction and generation, known for its fast and high-quality rendering capabilities. This paper introduces a novel diffusion-based framework, GVGEN, designed to efficiently generate 3D Gaussian representations from text input.
  arXiv  Detail & Related papers  (2024-03-19T17:57:52Z)
• VastGaussian: Vast 3D Gaussians for Large Scene Reconstruction [59.40711222096875]
  We present VastGaussian, the first method for high-quality reconstruction and real-time rendering on large scenes based on 3D Gaussian Splatting. Our approach outperforms existing NeRF-based methods and achieves state-of-the-art results on multiple large scene datasets.
  arXiv  Detail & Related papers  (2024-02-27T11:40:50Z)
• GES: Generalized Exponential Splatting for Efficient Radiance Field Rendering [112.16239342037714]
  GES (Generalized Exponential Splatting) is a novel representation that employs Generalized Exponential Function (GEF) to model 3D scenes. With the aid of a frequency-modulated loss, GES achieves competitive performance in novel-view synthesis benchmarks.
  arXiv  Detail & Related papers  (2024-02-15T17:32:50Z)
• Triplane Meets Gaussian Splatting: Fast and Generalizable Single-View 3D Reconstruction with Transformers [37.14235383028582]
  We introduce a novel approach for single-view reconstruction that efficiently generates a 3D model from a single image via feed-forward inference. Our method utilizes two transformer-based networks, namely a point decoder and a triplane decoder, to reconstruct 3D objects using a hybrid Triplane-Gaussian intermediate representation.
  arXiv  Detail & Related papers  (2023-12-14T17:18:34Z)
• GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting [51.96353586773191]
  We introduce textbfGS-SLAM that first utilizes 3D Gaussian representation in the Simultaneous Localization and Mapping system. Our method utilizes a real-time differentiable splatting rendering pipeline that offers significant speedup to map optimization and RGB-D rendering. Our method achieves competitive performance compared with existing state-of-the-art real-time methods on the Replica, TUM-RGBD datasets.
  arXiv  Detail & Related papers  (2023-11-20T12:08:23Z)

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.