GSCompleter: A Distillation-Free Plugin for Metric-Aware 3D Gaussian Splatting Completion in Seconds

• URL: http://arxiv.org/abs/2604.20155v1
• Date: Wed, 22 Apr 2026 03:47:25 GMT
• Title: GSCompleter: A Distillation-Free Plugin for Metric-Aware 3D Gaussian Splatting Completion in Seconds
• Abstract summary: GSCompleter is a distillation-free plugin that shifts scene completion to a stable "Generate-then-Register" workflow.<n>Our approach first synthesizes plausible 2D reference images and explicitly lifts them into metric-scale 3D primitives via a robust Stereo-Anchor mechanism.<n>This shift to a rapid registration paradigm delivers superior 3DGS completion performance across three distinct benchmarks.
• Score: 25.00064989891048
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While 3D Gaussian Splatting (3DGS) has revolutionized real-time rendering, its performance degrades significantly under sparse-view extrapolation, manifesting as severe geometric voids and artifacts. Existing solutions primarily rely on an iterative "Repair-then-Distill" paradigm, which is inherently unstable and prone to overfitting. In this work, we propose GSCompleter, a distillation-free plugin that shifts scene completion to a stable "Generate-then-Register" workflow. Our approach first synthesizes plausible 2D reference images and explicitly lifts them into metric-scale 3D primitives via a robust Stereo-Anchor mechanism. These primitives are then seamlessly integrated into the global context through a novel Ray-Constrained Registration strategy. This shift to a rapid registration paradigm delivers superior 3DGS completion performance across three distinct benchmarks, enhancing the quality and efficiency of various baselines and achieving new SOTA results.

Related papers

• REFINE: Super-efficient 3D Gaussian Splatting Pruning via Rendering-Free Primitive Importance [58.94345609736356]
  REFINE is a highly accelerated 3DGS pruning framework centered on a novel rendering-free primitive importance metric.<n>Experiments across multiple benchmark datasets demonstrate that REFINE maintains highly competitive rendering quality.
  arXiv  Detail & Related papers  (2026-06-08T06:16:38Z)
• NRGS: Neural Regularization for Robust 3D Semantic Gaussian Splatting [41.21645092733966]
  We propose a neural regularization method that refines the noisy 3D semantic field produced by lifting multi-view inconsistent 2D features.<n> Experiments show that our method enhances the accuracy of lifted semantics, providing an efficient and effective approach to robust 3D semantic Gaussian Splatting.
  arXiv  Detail & Related papers  (2026-04-24T10:57:05Z)
• PDF-GS: Progressive Distractor Filtering for Robust 3D Gaussian Splatting [32.97857587591785]
  We propose PDF-GS, a framework that amplifies 3DGS's inherent ability to suppress inconsistent signals.<n> PDF-GS achieves robust, high-fidelity, and distractor-free reconstructions, consistently outperforming baselines across diverse datasets and challenging real-world conditions.<n>Our approach is lightweight and easily adaptable to existing 3DGS frameworks, requiring no architectural changes or additional inference overhead, leading to a new state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-04-14T11:03:55Z)
• PointSplat: Efficient Geometry-Driven Pruning and Transformer Refinement for 3D Gaussian Splatting [2.5823344978857388]
  3D Gaussian Splatting has recently unlocked real-time, high-fidelity novel view synthesis by representing scenes using explicit 3D primitives.<n>Traditional methods often require millions of Gaussians to capture complex scenes, leading to significant memory and storage demands.<n>Recent approaches have addressed this issue through pruning and per-scene fine-tuning of Gaussian parameters, thereby reducing the model size while maintaining visual quality.
  arXiv  Detail & Related papers  (2026-04-10T20:56:18Z)
• GS^2: Graph-based Spatial Distribution Optimization for Compact 3D Gaussian Splatting [50.2045409672451]
  3D Gaussian Splatting (3DGS) has demonstrated breakthrough performance in novel view synthesis and real-time rendering.<n>It is constrained by the high memory cost due to a huge number of Gaussian points.<n>We propose graph-based spatial distribution optimization for compact 3DGS.
  arXiv  Detail & Related papers  (2026-04-02T10:41:51Z)
• OracleGS: Grounding Generative Priors for Sparse-View Gaussian Splatting [78.70702961852119]
  OracleGS reconciles generative completeness with regressive fidelity for sparse view Gaussian Splatting.<n>Our approach conditions the powerful generative prior on multi-view geometric evidence, filtering hallucinatory artifacts while preserving plausible completions in under-constrained regions.
  arXiv  Detail & Related papers  (2025-09-27T11:19:32Z)
• Interpretable Single-View 3D Gaussian Splatting using Unsupervised Hierarchical Disentangled Representation Learning [46.85417907244265]
  We propose an interpretable single-view 3DGS framework, termed 3DisGS, to discover both coarse- and fine-grained 3D semantics.<n>Our model achieves 3D disentanglement while preserving high-quality and rapid reconstruction.
  arXiv  Detail & Related papers  (2025-04-05T14:42:13Z)
• ResGS: Residual Densification of 3D Gaussian for Efficient Detail Recovery [21.259495668002796]
  We introduce a novel densification operation, residual split, which adds a downscaled Gaussian as a residual.<n>Our approach is capable of adaptively retrieving details and complementing missing geometry.
  arXiv  Detail & Related papers  (2024-12-10T13:19:27Z)
• GSD: View-Guided Gaussian Splatting Diffusion for 3D Reconstruction [52.04103235260539]
  We present a diffusion model approach based on Gaussian Splatting representation for 3D object reconstruction from a single view. The model learns to generate 3D objects represented by sets of GS ellipsoids. The final reconstructed objects explicitly come with high-quality 3D structure and texture, and can be efficiently rendered in arbitrary views.
  arXiv  Detail & Related papers  (2024-07-05T03:43:08Z)
• 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)
• 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)
• 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)

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.