A systematic study of 3DGS under sparse-view conditions and varying input resolutions identifies and quantify a trade-off: lower-resolution inputs facilitate stable global geometry reconstruction, whereas higher-resolution inputs enable finer detail recovery but introduce high-frequency artifacts and instability.
Results indicate that a direct, multi-attribute 3D consistency objective, when combined with high-quality correspondences, is effective for addressing the ill-posed sparse-view reconstruction problem.
Jinqian Yang, Yichen Wu, Wanhua Li et al.· arXiv.org· 1 citation
Existing 3D mesh reconstruction methods from Gaussian scene representations predominantly rely on iterative optimization, resulting in slow inference and limited scalability to high-resolution inputs. In this paper, we present AnyGS2Mesh, the first feed-forward framework for directly reconstructing 3D meshes from 3D Gaussian Splatting representations with support for arbitrary input image resolutions. Our approach incorporates a Gaussian-Guided Transformer architecture that exploits explicit 3D geometric priors for efficient mesh generation. We introduce three key components: (1) a Gaussian-Guided Spatial Reasoning Transformer represents Gaussian primitives as structured 3D tokens and jointly reasons over Gaussian and image features; (2) a Streaming and Patchwise Geometry Encoder processes native-resolution views sequentially and aggregates information across variable-length view sets; (3) a Scale-Aligned Hybrid Depth Refiner uses a PatchFusion-style encoder--decoder to fuse RGB-conditioned predicted depth with Gaussian-rendered metric depth, combining fine local structures with globally consistent metric scale. The refined depth maps are integrated through TSDF fusion, followed by Marching Cubes for deterministic mesh extraction. Extensive experiments show that AnyGS2Mesh achieves state-of-the-art reconstruction quality while significantly reducing inference time compared with optimization-based baselines, enabling near-real-time, high-quality mesh reconstruction. Our results demonstrate the potential of combining Gaussian representations and feed-forward Transformer architectures for scalable 3D geometry reconstruction. The code will be made publicly available upon acceptance.
Yuxuan Song, Fan Gao, Yi-Bo Zhao et al.· 0 citations
High-fidelity novel view synthesis from sparse inputs remains a significant challenge in 3D reconstruction. Traditional methods relying on Multi-layer Perceptrons (MLPs) often struggle with overfitting and inaccurate geometric representations, especially with limited training views. To address these issues, we introduce OC-CDGS, a novel framework leveraging Octree-structured 3D Gaussian Splatting (3DGS) with Combined Depth Regularization (CDR). OC-CDGS organizes sparse point clouds into hierarchical octree-structured anchors, enabling efficient decoding of neural Gaussian attributes through compact MLPs. The CDR approach integrates global structure, local details, and gradient consistency to enhance geometric supervision. Additionally, we incorporate a Neural Gaussian Random Dropout Strategy (NGDS) to improve representation robustness and a Scene-Extent- Based Anchor Densification Strategy (SADS) to enhance anchor coverage in peripheral regions. Extensive experiments on MipNeRF360 and LLFF datasets demonstrate that OC-CDGS achieves state-of-the-art performance with real-time rendering capabilities, preserving high-quality geometric details.
This work proposes a novel 3D-aware video restoration framework designed to enhance the quality of sparse 3DGS reconstruction and introduces a camera-conditioned geometric prior that guides the network toward geometrically grounded restoration that remains coherent across viewpoints.
Xinhui Liu, Can Wang, Wei Jiang et al.· 0 citations
This work proposes an Iterative Spatial Decomposition framework that bridges dense geometric priors from Multi-View Stereo (MVS) with Gaussian Splatting and introduces Hierarchical Geometric Prior Sampling (HGPS), which substantially reduce redundancy in MVS point clouds while preserving critical details, thereby providing a more robust geometric foundation for reconstruction.
Zonghua Yu, Junhuai Li, Huaijun Wang et al.· ACM Transactions on Multimed...· 0 citations
A semantic-guided 3D Gaussian splatting (3DGS) framework tailored to sparse-view industrial reconstruction was introduced, enabling robust reconstruction from limited viewpoints and offers a practical geometric foundation for automated inspection and remote equipment monitoring.
Boyang Li, Tian-Han Gao, Zuan Gu et al.· Visual Computing for Industr...· 0 citations
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