3DGS-VBench: A Comprehensive Video Quality Evaluation Benchmark for 3DGS Compression

• URL: http://arxiv.org/abs/2508.07038v1
• Date: Sat, 09 Aug 2025 16:47:19 GMT
• Title: 3DGS-VBench: A Comprehensive Video Quality Evaluation Benchmark for 3DGS Compression
• Authors: Yuke Xing, William Gordon, Qi Yang, Kaifa Yang, Jiarui Wang, Yiling Xu,
• Abstract summary: 3DGS-VBench is a large-scale Video Quality Assessment (VQA) dataset and Benchmark with 660 compressed 3DGS models and video sequences generated from 11 scenes.<n>We benchmark 6 3DGS compression algorithms on storage efficiency and visual quality, and evaluate 15 quality assessment metrics across multiple paradigms.
• Score: 9.900921811495213
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D Gaussian Splatting (3DGS) enables real-time novel view synthesis with high visual fidelity, but its substantial storage requirements hinder practical deployment, prompting state-of-the-art (SOTA) 3DGS methods to incorporate compression modules. However, these 3DGS generative compression techniques introduce unique distortions lacking systematic quality assessment research. To this end, we establish 3DGS-VBench, a large-scale Video Quality Assessment (VQA) Dataset and Benchmark with 660 compressed 3DGS models and video sequences generated from 11 scenes across 6 SOTA 3DGS compression algorithms with systematically designed parameter levels. With annotations from 50 participants, we obtained MOS scores with outlier removal and validated dataset reliability. We benchmark 6 3DGS compression algorithms on storage efficiency and visual quality, and evaluate 15 quality assessment metrics across multiple paradigms. Our work enables specialized VQA model training for 3DGS, serving as a catalyst for compression and quality assessment research. The dataset is available at https://github.com/YukeXing/3DGS-VBench.

Related papers

• CSGaussian: Progressive Rate-Distortion Compression and Segmentation for 3D Gaussian Splatting [57.73006852239138]
  We present the first unified framework for rate-distortion-optimized compression and segmentation of 3D Gaussian Splatting (3DGS)<n>Inspired by recent advances in rate-distortion-optimized 3DGS compression, this work integrates semantic learning into the compression pipeline to support decoder-side applications.<n>Our scheme features a lightweight implicit neural representation-based hyperprior, enabling efficient entropy coding of both color and semantic attributes.
  arXiv  Detail & Related papers  (2026-01-19T08:21:45Z)
• Splatwizard: A Benchmark Toolkit for 3D Gaussian Splatting Compression [69.94586602640355]
  We introduce Splatwizard, a unified benchmark toolkit designed specifically for 3DGS compression models.<n>Splatwizard provides an easy-to-use framework to implement new 3DGS compression model and utilize state-of-the-art techniques.
  arXiv  Detail & Related papers  (2025-12-31T09:26:04Z)
• Perceptual Quality Assessment of 3D Gaussian Splatting: A Subjective Dataset and Prediction Metric [76.66966098297986]
  We present 3DGS-QA, the first subjective quality assessment dataset for 3DGS.<n>It comprises 225 degraded reconstructions across 15 object types, enabling a controlled investigation of common distortion factors.<n>Our model extracts spatial and photometric cues from the Gaussian representation to estimate perceived quality in a structure-aware manner.
  arXiv  Detail & Related papers  (2025-11-11T09:34:20Z)
• GFix: Perceptually Enhanced Gaussian Splatting Video Compression [16.637492219941446]
  Existing 3DGS-based video codecs exhibit more noticeable visual artifacts and relatively low compression ratios.<n>In this paper, we specifically target the perceptual enhancement of 3DGS-based video compression, based on the assumption that artifacts from 3DGS rendering and quantization resemble noisy latents sampled during diffusion training.<n>We propose a content-adaptive framework, GFix, comprising a streamlined, single-step diffusion model that serves as an off-the-shelf neural enhancer.
  arXiv  Detail & Related papers  (2025-11-10T11:03:30Z)
• Distilled-3DGS:Distilled 3D Gaussian Splatting [49.098181805161275]
  We propose the first knowledge distillation framework for 3DGS.<n>It features various teacher models, including vanilla 3DGS, noise-augmented variants, and dropout-regularized versions.<n>It achieves promising rendering results in both rendering quality and storage efficiency compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-08-19T17:59:26Z)
• A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation [66.62489208150681]
  3D Gaussian Splatting (3DGS) has emerged as a powerful alternative to Neural Radiance Fields (NeRF) for 3D scene representation.<n>This survey provides a comprehensive overview of recent progress in 3DGS applications.
  arXiv  Detail & Related papers  (2025-08-13T17:44:39Z)
• 3DGabSplat: 3D Gabor Splatting for Frequency-adaptive Radiance Field Rendering [50.04967868036964]
  3D Gaussian Splatting (3DGS) has enabled real-time rendering while maintaining high-fidelity novel view synthesis.<n>We propose 3D Gabor Splatting (3DGabSplat) that incorporates a novel 3D Gabor-based primitive with multiple directional 3D frequency responses.<n>We achieve 1.35 dBR gain over 3D with simultaneously reduced number of primitive memory consumption.
  arXiv  Detail & Related papers  (2025-08-07T12:49:44Z)
• Adaptive 3D Gaussian Splatting Video Streaming [28.283254336752602]
  We introduce an innovative framework for 3DGS volumetric video streaming.<n>By employing hybrid saliency tiling and differentiated quality modeling, we achieve efficient data compression and adaptation to bandwidth fluctuations.<n>Our method demonstrated superiority over existing approaches in various aspects, including video quality, compression effectiveness, and transmission rate.
  arXiv  Detail & Related papers  (2025-07-19T01:45:24Z)
• 3DGS-IEval-15K: A Large-scale Image Quality Evaluation Database for 3D Gaussian-Splatting [38.666247000427674]
  3DGS has emerged as a promising approach for novel view synthesis, offering real-time rendering with high visual fidelity.<n>We present 3DGS-IEval-15K, the first large-scale image quality assessment (IQA) dataset specifically designed for compressed 3DGS representations.
  arXiv  Detail & Related papers  (2025-06-17T15:39:34Z)
• 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)
• 3DGS.zip: A survey on 3D Gaussian Splatting Compression Methods [10.122120872952296]
  3D Gaussian Splatting (3DGS) has emerged as a cutting-edge technique for real-time radiance field rendering.<n>Despite its advantages in rendering speed and image fidelity, 3DGS is limited by its significant storage and memory demands.<n>This survey provides a detailed examination of compression and compaction techniques developed to make 3DGS more efficient.
  arXiv  Detail & Related papers  (2024-06-17T11:43:38Z)
• PUP 3D-GS: Principled Uncertainty Pruning for 3D Gaussian Splatting [59.277480452459315]
  We propose a principled sensitivity pruning score that preserves visual fidelity and foreground details at significantly higher compression ratios.<n>We also propose a multi-round prune-refine pipeline that can be applied to any pretrained 3D-GS model without changing its training pipeline.
  arXiv  Detail & Related papers  (2024-06-14T17:53:55Z)
• DOGS: Distributed-Oriented Gaussian Splatting for Large-Scale 3D Reconstruction Via Gaussian Consensus [56.45194233357833]
  We propose DoGaussian, a method that trains 3DGS distributedly. Our method accelerates the training of 3DGS by 6+ times when evaluated on large-scale scenes.
  arXiv  Detail & Related papers  (2024-05-22T19:17:58Z)
• SAGS: Structure-Aware 3D Gaussian Splatting [53.6730827668389]
  We propose a structure-aware Gaussian Splatting method (SAGS) that implicitly encodes the geometry of the scene. SAGS reflects to state-of-the-art rendering performance and reduced storage requirements on benchmark novel-view synthesis datasets.
  arXiv  Detail & Related papers  (2024-04-29T23:26:30Z)

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.