S2Gaussian: Sparse-View Super-Resolution 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2503.04314v1
• Date: Thu, 06 Mar 2025 10:58:26 GMT
• Title: S2Gaussian: Sparse-View Super-Resolution 3D Gaussian Splatting
• Authors: Yecong Wan, Mingwen Shao, Yuanshuo Cheng, Wangmeng Zuo,
• Abstract summary: We propose a novel Sparse-view Super-resolution 3D Gaussian Splatting framework, dubbed S2Gaussian, that can reconstruct structure-accurate and detail-faithful 3D scenes with only sparse and low-resolution views.<n>Experiments demonstrate superior results and in particular establishing new state-of-the-art performances with more consistent geometry and finer details.
• Score: 47.75073170368562
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we aim ambitiously for a realistic yet challenging problem, namely, how to reconstruct high-quality 3D scenes from sparse low-resolution views that simultaneously suffer from deficient perspectives and clarity. Whereas existing methods only deal with either sparse views or low-resolution observations, they fail to handle such hybrid and complicated scenarios. To this end, we propose a novel Sparse-view Super-resolution 3D Gaussian Splatting framework, dubbed S2Gaussian, that can reconstruct structure-accurate and detail-faithful 3D scenes with only sparse and low-resolution views. The S2Gaussian operates in a two-stage fashion. In the first stage, we initially optimize a low-resolution Gaussian representation with depth regularization and densify it to initialize the high-resolution Gaussians through a tailored Gaussian Shuffle Split operation. In the second stage, we refine the high-resolution Gaussians with the super-resolved images generated from both original sparse views and pseudo-views rendered by the low-resolution Gaussians. In which a customized blur-free inconsistency modeling scheme and a 3D robust optimization strategy are elaborately designed to mitigate multi-view inconsistency and eliminate erroneous updates caused by imperfect supervision. Extensive experiments demonstrate superior results and in particular establishing new state-of-the-art performances with more consistent geometry and finer details.

Related papers

• CrossView-GS: Cross-view Gaussian Splatting For Large-scale Scene Reconstruction [5.528874948395173]
  3D Gaussian Splatting (3DGS) has emerged as a prominent method for scene representation and reconstruction.<n>We propose a novel cross-view Gaussian Splatting method for large-scale scene reconstruction, based on dual-branch fusion.<n>Our method achieves superior performance in novel view synthesis compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-01-03T08:24:59Z)
• TSGaussian: Semantic and Depth-Guided Target-Specific Gaussian Splatting from Sparse Views [18.050257821756148]
  TSGaussian is a novel framework that combines semantic constraints with depth priors to avoid geometry degradation in novel view synthesis tasks. Our approach prioritizes computational resources on designated targets while minimizing background allocation. Extensive experiments demonstrate that TSGaussian outperforms state-of-the-art methods on three standard datasets.
  arXiv  Detail & Related papers  (2024-12-13T11:26:38Z)
• GPS-Gaussian+: Generalizable Pixel-wise 3D Gaussian Splatting for Real-Time Human-Scene Rendering from Sparse Views [67.34073368933814]
  We propose a generalizable Gaussian Splatting approach for high-resolution image rendering under a sparse-view camera setting. We train our Gaussian parameter regression module on human-only data or human-scene data, jointly with a depth estimation module to lift 2D parameter maps to 3D space. Experiments on several datasets demonstrate that our method outperforms state-of-the-art methods while achieving an exceeding rendering speed.
  arXiv  Detail & Related papers  (2024-11-18T08:18:44Z)
• 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)
• PF3plat: Pose-Free Feed-Forward 3D Gaussian Splatting [54.7468067660037]
  PF3plat sets a new state-of-the-art across all benchmarks, supported by comprehensive ablation studies validating our design choices. Our framework capitalizes on fast speed, scalability, and high-quality 3D reconstruction and view synthesis capabilities of 3DGS.
  arXiv  Detail & Related papers  (2024-10-29T15:28:15Z)
• SRGS: Super-Resolution 3D Gaussian Splatting [14.26021476067791]
  We propose Super-Resolution 3D Gaussian Splatting (SRGS) to perform the optimization in a high-resolution (HR) space. The sub-pixel constraint is introduced for the increased viewpoints in HR space, exploiting the sub-pixel cross-view information of the multiple low-resolution (LR) views. Our method achieves high rendering quality on HRNVS only with LR inputs, outperforming state-of-the-art methods on challenging datasets such as Mip-NeRF 360 and Tanks & Temples.
  arXiv  Detail & Related papers  (2024-04-16T06:58:30Z)
• CoherentGS: Sparse Novel View Synthesis with Coherent 3D Gaussians [18.42203035154126]
  We introduce a structured Gaussian representation that can be controlled in 2D image space.<n>We then constraint the Gaussians, in particular their position, and prevent them from moving independently during optimization.<n>We demonstrate significant improvements compared to the state-of-the-art sparse-view NeRF-based approaches on a variety of scenes.
  arXiv  Detail & Related papers  (2024-03-28T15:27:13Z)
• 2D Gaussian Splatting for Geometrically Accurate Radiance Fields [50.056790168812114]
  3D Gaussian Splatting (3DGS) has recently revolutionized radiance field reconstruction, achieving high quality novel view synthesis and fast rendering speed without baking. We present 2D Gaussian Splatting (2DGS), a novel approach to model and reconstruct geometrically accurate radiance fields from multi-view images. We demonstrate that our differentiable terms allows for noise-free and detailed geometry reconstruction while maintaining competitive appearance quality, fast training speed, and real-time rendering.
  arXiv  Detail & Related papers  (2024-03-26T17:21:24Z)
• GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting for Real-time Human Novel View Synthesis [70.24111297192057]
  We present a new approach, termed GPS-Gaussian, for synthesizing novel views of a character in a real-time manner. The proposed method enables 2K-resolution rendering under a sparse-view camera setting.
  arXiv  Detail & Related papers  (2023-12-04T18:59:55Z)
• Neural Parametric Gaussians for Monocular Non-Rigid Object Reconstruction [8.260048622127913]
  Reconstructing dynamic objects from monocular videos is a severely underconstrained and challenging problem. We introduce Neural Parametric Gaussians (NPGs) to take on this challenge by imposing a two-stage approach. NPGs achieve superior results compared to previous works, especially in challenging scenarios with few multi-view cues.
  arXiv  Detail & Related papers  (2023-12-02T18:06:24Z)

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.