GS2E: Gaussian Splatting is an Effective Data Generator for Event Stream Generation

• URL: http://arxiv.org/abs/2505.15287v1
• Date: Wed, 21 May 2025 09:15:42 GMT
• Title: GS2E: Gaussian Splatting is an Effective Data Generator for Event Stream Generation
• Authors: Yuchen Li, Chaoran Feng, Zhenyu Tang, Kaiyuan Deng, Wangbo Yu, Yonghong Tian, Li Yuan,
• Abstract summary: We introduce GS2E (Gaussian Splatting to Event), a large-scale synthetic event dataset for high-fidelity event vision tasks.<n>Results on event-based 3D reconstruction demonstrate GS2E's superior generalization capabilities and its practical value.
• Score: 32.13436507983477
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We introduce GS2E (Gaussian Splatting to Event), a large-scale synthetic event dataset for high-fidelity event vision tasks, captured from real-world sparse multi-view RGB images. Existing event datasets are often synthesized from dense RGB videos, which typically lack viewpoint diversity and geometric consistency, or depend on expensive, difficult-to-scale hardware setups. GS2E overcomes these limitations by first reconstructing photorealistic static scenes using 3D Gaussian Splatting, and subsequently employing a novel, physically-informed event simulation pipeline. This pipeline generally integrates adaptive trajectory interpolation with physically-consistent event contrast threshold modeling. Such an approach yields temporally dense and geometrically consistent event streams under diverse motion and lighting conditions, while ensuring strong alignment with underlying scene structures. Experimental results on event-based 3D reconstruction demonstrate GS2E's superior generalization capabilities and its practical value as a benchmark for advancing event vision research.

Related papers

• HoliGS: Holistic Gaussian Splatting for Embodied View Synthesis [59.25751939710903]
  We propose a novel deformable Gaussian splatting framework that addresses embodied view synthesis from long monocular RGB videos.<n>Our method leverages invertible Gaussian Splatting deformation networks to reconstruct large-scale, dynamic environments accurately.<n>Results highlight a practical and scalable solution for EVS in real-world scenarios.
  arXiv  Detail & Related papers  (2025-06-24T03:54:40Z)
• HybridGS: Decoupling Transients and Statics with 2D and 3D Gaussian Splatting [47.67153284714988]
  We propose a novel hybrid representation, termed as HybridGS, using 2D Gaussians for transient objects per image.<n>We also propose a straightforward yet effective multi-stage training strategy to ensure robust training and high-quality view synthesis.<n> Experiments on benchmark datasets show our state-of-the-art performance of novel view synthesis in both indoor and outdoor scenes.
  arXiv  Detail & Related papers  (2024-12-05T03:20:35Z)
• DeSiRe-GS: 4D Street Gaussians for Static-Dynamic Decomposition and Surface Reconstruction for Urban Driving Scenes [71.61083731844282]
  We present DeSiRe-GS, a self-supervised gaussian splatting representation. It enables effective static-dynamic decomposition and high-fidelity surface reconstruction in complex driving scenarios.
  arXiv  Detail & Related papers  (2024-11-18T05:49:16Z)
• CityGaussianV2: Efficient and Geometrically Accurate Reconstruction for Large-Scale Scenes [53.107474952492396]
  CityGaussianV2 is a novel approach for large-scale scene reconstruction.<n>We implement a decomposed-gradient-based densification and depth regression technique to eliminate blurry artifacts and accelerate convergence.<n>Our method strikes a promising balance between visual quality, geometric accuracy, as well as storage and training costs.
  arXiv  Detail & Related papers  (2024-11-01T17:59:31Z)
• EF-3DGS: Event-Aided Free-Trajectory 3D Gaussian Splatting [72.60992807941885]
  Event cameras, inspired by biological vision, record pixel-wise intensity changes asynchronously with high temporal resolution.<n>We propose Event-Aided Free-Trajectory 3DGS, which seamlessly integrates the advantages of event cameras into 3DGS.<n>We evaluate our method on the public Tanks and Temples benchmark and a newly collected real-world dataset, RealEv-DAVIS.
  arXiv  Detail & Related papers  (2024-10-20T13:44:24Z)
• Elite-EvGS: Learning Event-based 3D Gaussian Splatting by Distilling Event-to-Video Priors [8.93657924734248]
  Event cameras are bio-inspired sensors that output asynchronous and sparse event streams, instead of fixed frames. We propose a novel event-based 3DGS framework, named Elite-EvGS. Our key idea is to distill the prior knowledge from the off-the-shelf event-to-video (E2V) models to effectively reconstruct 3D scenes from events.
  arXiv  Detail & Related papers  (2024-09-20T10:47:52Z)
• E2GS: Event Enhanced Gaussian Splatting [9.096805985896625]
  Event Enhanced Gaussian Splatting (E2GS) is a novel method that incorporates event data into Gaussian Splatting. Our E2GS effectively utilizes both blurry images and event data, significantly improving image deblurring and producing high-quality novel view synthesis.
  arXiv  Detail & Related papers  (2024-06-21T08:43:47Z)
• Event3DGS: Event-Based 3D Gaussian Splatting for High-Speed Robot Egomotion [54.197343533492486]
  Event3DGS can reconstruct high-fidelity 3D structure and appearance under high-speed egomotion. Experiments on multiple synthetic and real-world datasets demonstrate the superiority of Event3DGS compared with existing event-based dense 3D scene reconstruction frameworks. Our framework also allows one to incorporate a few motion-blurred frame-based measurements into the reconstruction process to further improve appearance fidelity without loss of structural accuracy.
  arXiv  Detail & Related papers  (2024-06-05T06:06:03Z)
• EvGGS: A Collaborative Learning Framework for Event-based Generalizable Gaussian Splatting [5.160735014509357]
  We propose the first event-based generalizable 3D reconstruction framework, called EvGGS. It reconstructs scenes as 3D Gaussians from only event input in a feedforward manner. Our approach performs better than all baselines in reconstruction quality, and depth/intensity predictions with satisfactory speed.
  arXiv  Detail & Related papers  (2024-05-23T18:10:26Z)
• 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)
• Implicit Event-RGBD Neural SLAM [54.74363487009845]
  Implicit neural SLAM has achieved remarkable progress recently. Existing methods face significant challenges in non-ideal scenarios. We propose EN-SLAM, the first event-RGBD implicit neural SLAM framework.
  arXiv  Detail & Related papers  (2023-11-18T08:48:58Z)

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.