mlsanityData to 14 Oct 2026

EyeNavGS: A 6-DoF Navigation Dataset and Record-n-Replay Software for Real-World 3DGS Scenes in VR

arXiv 2506.02380 ↗First posted 3 Jun 2025cs.MM cs.CV cs.GRcited by 0

Not on the map. Why?

Abstract (arXiv)

3D Gaussian Splatting (3DGS) is an emerging media representation that reconstructs real-world 3D scenes in high fidelity, enabling 6-degrees-of-freedom (6-DoF) navigation in virtual reality (VR). However, developing and evaluating 3DGS-enabled applications and optimizing their rendering performance, require realistic user navigation data. Such data is currently unavailable for photorealistic 3DGS reconstructions of real-world scenes. This paper introduces EyeNavGS (EyeNavGS), the first publicly available 6-DoF navigation dataset featuring traces from 46 participants exploring twelve diverse, real-world 3DGS scenes. The dataset was collected at two sites, using the Meta Quest Pro headsets, recording the head pose and eye gaze data for each rendered frame during free world standing 6-DoF navigation. For each of the twelve scenes, we performed careful scene initialization to correct for scene tilt and scale, ensuring a perceptually-comfortable VR experience. We also release our open-source SIBR viewer software fork with record-and-replay functionalities and a suite of utility tools for data processing, conversion, and visualization. The EyeNavGS dataset and its accompanying software tools provide valuable resources for advancing research in 6-DoF viewport prediction, adaptive streaming, 3D saliency, and foveated rendering for 3DGS scenes. The EyeNavGS dataset is available at: https://symmru.github.io/EyeNavGS/.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 43 references that are in the map. First posted on arXiv 3 Jun 2025.

Builds on

15 of 43 references.

References · 15

  1. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
  2. 20242D Gaussian Splatting for Geometrically Accurate Radiance Fieldscited by 1,076
  3. 2023Nerfstudio: A Modular Framework for Neural Radiance Field Developmentcited by 600
  4. 2024A Hierarchical 3D Gaussian Representation for Real-Time Rendering of Very Large Datasetscited by 216
  5. 2024Mini-Splatting: Representing Scenes with a Constrained Number of Gaussianscited by 213
  6. 2024Reducing the Memory Footprint of 3D Gaussian Splattingcited by 133
  7. 2023SMERF: Streamable Memory Efficient Radiance Fields for Real-Time Large-Scene Explorationcited by 62
  8. 2024VR-Splatting: Foveated Radiance Field Rendering via 3D Gaussian Splatting and Neural Pointscited by 29
  9. 2024MetaSapiens: Real-Time Neural Rendering with Efficiency-Aware Pruning and Accelerated Foveated Renderingcited by 27
  10. 2025Sketch and Patch: Efficient 3D Gaussian Representation for Man-Made Scenescited by 20
  11. 2025L3GS: Layered 3D Gaussian Splats for Efficient 3D Scene Deliverycited by 6
  12. 2023Towards Attention-aware Foveated Renderingcited by 5
3 more
  1. 2023FSVVD: A Dataset of Full Scene Volumetric Videocited by 5
  2. 2020Kalman Filter-based Head Motion Prediction for Cloud-based Mixed Realitycited by 1
  3. 2022VR Viewport Pose Model for Quantifying and Exploiting Frame Correlationscited by 1

Built on by

Not cited yet.