VRSplat: Fast and Robust Gaussian Splatting for Virtual Reality

• URL: http://arxiv.org/abs/2505.10144v1
• Date: Thu, 15 May 2025 10:17:48 GMT
• Title: VRSplat: Fast and Robust Gaussian Splatting for Virtual Reality
• Authors: Xuechang Tu, Lukas Radl, Michael Steiner, Markus Steinberger, Bernhard Kerbl, Fernando de la Torre,
• Abstract summary: We introduce VRSplat: we combine and extend several recent advancements in 3DGS to address challenges of VR holistically.<n> VRSplat is the first, systematically evaluated 3DGS approach capable of supporting modern VR applications, achieving 72+ FPS while eliminating popping and stereo-disrupting floaters.
• Score: 47.738522999465864
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 3D Gaussian Splatting (3DGS) has rapidly become a leading technique for novel-view synthesis, providing exceptional performance through efficient software-based GPU rasterization. Its versatility enables real-time applications, including on mobile and lower-powered devices. However, 3DGS faces key challenges in virtual reality (VR): (1) temporal artifacts, such as popping during head movements, (2) projection-based distortions that result in disturbing and view-inconsistent floaters, and (3) reduced framerates when rendering large numbers of Gaussians, falling below the critical threshold for VR. Compared to desktop environments, these issues are drastically amplified by large field-of-view, constant head movements, and high resolution of head-mounted displays (HMDs). In this work, we introduce VRSplat: we combine and extend several recent advancements in 3DGS to address challenges of VR holistically. We show how the ideas of Mini-Splatting, StopThePop, and Optimal Projection can complement each other, by modifying the individual techniques and core 3DGS rasterizer. Additionally, we propose an efficient foveated rasterizer that handles focus and peripheral areas in a single GPU launch, avoiding redundant computations and improving GPU utilization. Our method also incorporates a fine-tuning step that optimizes Gaussian parameters based on StopThePop depth evaluations and Optimal Projection. We validate our method through a controlled user study with 25 participants, showing a strong preference for VRSplat over other configurations of Mini-Splatting. VRSplat is the first, systematically evaluated 3DGS approach capable of supporting modern VR applications, achieving 72+ FPS while eliminating popping and stereo-disrupting floaters.

Related papers

• Virtual Memory for 3D Gaussian Splatting [1.278093617645299]
  Gaussian Splatting represents a breakthrough in the field of novel view rendering.<n>Recent advances have increased the size of Splatting scenes that can be created.
  arXiv  Detail & Related papers  (2025-06-24T08:31:33Z)
• LODGE: Level-of-Detail Large-Scale Gaussian Splatting with Efficient Rendering [68.93333348474988]
  We present a novel level-of-detail (LOD) method for 3D Gaussian Splatting on memory-constrained devices.<n>Our approach iteratively selects optimal subsets of Gaussians based on camera distance.<n>Our method achieves state-of-the-art performance on both outdoor (Hierarchical 3DGS) and indoor (Zip-NeRF) datasets.
  arXiv  Detail & Related papers  (2025-05-29T06:50:57Z)
• EVolSplat: Efficient Volume-based Gaussian Splatting for Urban View Synthesis [61.1662426227688]
  Existing NeRF and 3DGS-based methods show promising results in achieving photorealistic renderings but require slow, per-scene optimization.<n>We introduce EVolSplat, an efficient 3D Gaussian Splatting model for urban scenes that works in a feed-forward manner.
  arXiv  Detail & Related papers  (2025-03-26T02:47:27Z)
• VRsketch2Gaussian: 3D VR Sketch Guided 3D Object Generation with Gaussian Splatting [17.92139776515526]
  We propose VRSketch2Gaussian, a first VR sketch-guided, multi-modal, native 3D object generation framework.<n>VRSS is the first large-scale paired dataset containing VR sketches, text, images, and 3DGS.
  arXiv  Detail & Related papers  (2025-03-16T07:03:13Z)
• Temporally Compressed 3D Gaussian Splatting for Dynamic Scenes [46.64784407920817]
  Temporally Compressed 3D Gaussian Splatting (TC3DGS) is a novel technique designed specifically to compress dynamic 3D Gaussian representations.<n>Our experiments across multiple datasets demonstrate that TC3DGS achieves up to 67$times$ compression with minimal or no degradation in visual quality.
  arXiv  Detail & Related papers  (2024-12-07T17:03:09Z)
• 3D Convex Splatting: Radiance Field Rendering with 3D Smooth Convexes [87.01284850604495]
  We introduce 3D Convexting (3DCS), which leverages 3D smooth convexes as primitives for modeling geometrically-meaningful radiance fields from multiview images. 3DCS achieves superior performance over 3DGS on benchmarks such as MipNeizer, Tanks and Temples, and Deep Blending. Our results highlight the potential of 3D Convexting to become the new standard for high-quality scene reconstruction.
  arXiv  Detail & Related papers  (2024-11-22T14:31:39Z)
• VR-Splatting: Foveated Radiance Field Rendering via 3D Gaussian Splatting and Neural Points [4.962171160815189]
  We propose a novel hybrid approach that combines the strengths of both point rendering directions regarding performance sweet spots.<n>For the fovea only, we use neural points with a convolutional neural network for the small pixel footprint, which provides sharp, detailed output.<n>Our evaluation confirms that our approach increases sharpness and details compared to a standard VR-ready 3DGS configuration.
  arXiv  Detail & Related papers  (2024-10-23T14:54:48Z)
• GaussianPro: 3D Gaussian Splatting with Progressive Propagation [49.918797726059545]
  3DGS relies heavily on the point cloud produced by Structure-from-Motion (SfM) techniques. We propose a novel method that applies a progressive propagation strategy to guide the densification of the 3D Gaussians. Our method significantly surpasses 3DGS on the dataset, exhibiting an improvement of 1.15dB in terms of PSNR.
  arXiv  Detail & Related papers  (2024-02-22T16:00:20Z)
• Towards Fast, Accurate and Stable 3D Dense Face Alignment [73.01620081047336]
  We propose a novel regression framework named 3DDFA-V2 which makes a balance among speed, accuracy and stability. We present a virtual synthesis method to transform one still image to a short-video which incorporates in-plane and out-of-plane face moving.
  arXiv  Detail & Related papers  (2020-09-21T15:37:37Z)

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.