Jul 2026· The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences· 0 citations· 8 references
Abstract
Abstract. 3D Gaussian Splatting (3DGS) has emerged as a leading technique for novel view synthesis (NVS), yet its performance degrades drastically under sparse-view conditions. While existing methods have sought to address this by incorporating accurate 3D geometry via Multi-View Stereo (MVS) or LiDAR priors, the view-dependent appearance parameters (i.e., spherical harmonics) remain exclusively optimized on the limited training views, leading to severe appearance overfitting. This is the fundamental reason why these geometry-enhanced methods still fail to generalize to out-of-distribution (OOD) viewpoints with large baselines, such as lane-changing trajectories in autonomous driving. To address this limitation, we propose VISTA-GS (Virtual Image Synthesis and Training Augmentation), a framework that synergizes MVS-based dense initialization with a physically-grounded virtual view augmentation strategy. Specifically, we position virtual cameras at strategic offsets around the original viewpoints and render virtual training images with binary validity masks via alpha-blending. By computing photometric losses exclusively within valid mask regions, VISTA-GS injects explicit angular constraints into the optimization process, effectively regularizing view-dependent appearance without relying on any external generative model. Experiments on the LLFF benchmark and a real-world LiDAR-scanned dataset demonstrate that our method achieves state-of-the-art NVS quality under sparse-view settings, with particularly significant improvements on challenging OOD viewpoints.
SARG-GS is proposed, a geometry-driven 3DGS framework tailored for sparse-view scenarios, comprising a Semantic Augmented Epipolar Fusion (SAEF) module and a Residual Guided Reprojection Compensation (RRC) module, which achieves superior structural completeness and rendering fidelity with as few as three input views.
Huan Zhou, Huizhi Zhu, Jiongming Qin et al.· The Visual Computer· 0 citations
Generating photorealistic novel views from unposed images requires both 3D geometric understanding and the ability to synthesize unseen content. A natural strategy combines feed-forward 3DGS reconstruction with multi-view diffusion. Yet prior pipelines extract at most one signal from the reconstruction, either pixel rendering or learned features, while none exploits per-Gaussian visibility for occlusion-aware reference selection. This *information disconnect* leaves renderable geometry, visibility cues, and learned features unused. SplatGuide closes this disconnect by reusing a single 3DGS scene across three complementary roles. Rendered images provide pixel-aligned geometric conditioning. Per-Gaussian source-view indices are rendered into a target-view voting map for occlusion-aware reference selection. Reconstruction tokens supply feature-level guidance via cross-attention. All three signals derive from the same reconstruction forward pass. Across RealEstate10K, DL3DV, Tanks-and-Temples, and Mip-NeRF 360, SplatGuide achieves state-of-the-art pose-free novel view synthesis. On RealEstate10K, with a moderate number of input views, it surpasses the ground-truth-pose baseline.
Yejun Zhang, Zihan Wang, Xuesi Ji et al.· 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, Tianhan Gao, Zuan Gu et al.· Visual Computing for Industr...· 0 citations
Standard 3D Gaussian Splatting (3DGS) pipelines for Novel View Synthesis (NVS) are bottlenecked by Structurefrom-Motion (SfM) initialization. In casual, sparse-view scenarios, feature matching breaks down, causing the entire reconstruction process to fail. We replace this brittle dependency with a COLMAP-free, feed-forward initializer powered by a Visual Geometry Grounded Transformer (VGGT). By leveraging VGGT, our pipeline jointly estimates camera parameters and dense scene geometry across all views in a single pass. A Bridge Module then robustly normalizes the scene scale and conditions initial Gaussian opacity on geometric confidence to discourage floater artifacts during densification. Our framework reduces the initialization phase from minutes (full-scene SfM) to seconds and achieves $\mathbf{1 0 0} \boldsymbol{\%}$ initialization success from as few as three unposed images (a regime where COLMAP succeeds on only 1 of 7 Mip-NeRF 360 scenes). Project page: https://github.com/yuvanrajkrishna/VGGT-Sparse-3DGS.
Shreeya Venkatraman, Yuvan Raj Krishna, J. C. V.· International Conference on...· 0 citations
3D Gaussian Splatting (3DGS) has achieved remarkable success in novel view synthesis; however, reconstructions under sparse views often exhibit noticeable artifacts. While recent video diffusion models provide strong spatio-temporal priors for 3DGS restoration, directly fine-tuning them for restoration is suboptimal, as they lack awareness of the underlying multi-camera geometry, resulting in multi-view inconsistencies. In this work, we propose a novel 3D-aware video restoration framework designed to enhance the quality of sparse 3DGS reconstruction. Specifically, we construct a large-scale 3DGS video dataset to enable specialized fine-tuning. To bridge the gap between 2D video generation and 3D multi-view constraints, we introduce a camera-conditioned geometric prior. By using the first and last frames as boundary anchors and encoding the corresponding camera relationships, we explicitly inject spatial structure into the video generation pipeline. This boundary-anchored, camera-aware prior guides the network toward geometrically grounded restoration that remains coherent across viewpoints. Extensive experiments show that, among video-prior restoration methods, our approach attains the best pixel- and structure-level fidelity (PSNR/SSIM) and improves multi-view consistency, while remaining competitive in perceptual quality (LPIPS).
Xinhui Liu, Can Wang, Wei Jiang et al.· 0 citations
Recent advances in 3D Gaussian Splatting (3DGS) have enabled high-quality, render-ready scene representations for novel-view synthesis. However, most existing 3DGS pipelines rely on multi-view observations (or non-causal access to future frames) to achieve sufficient coverage, which is often unavailable in on-device robotics and AR settings where sensing is restricted to a single stereo rig. Recovering a high-quality 3DGS scene from one stereo observation, therefore, remains challenging due to occlusions, limited field of view, and missing geometry. We present StereoSplat+, a diffusion-enhanced feed-forward framework that enables causal reconstruction from a single stereo pair. Our method builds on two key components. First, we propose StereoSplat, an input-invariant feed-forward 3D Gaussian estimator that takes a variable number of posed stereo pairs as input and predicts high-quality 3D Gaussians. StereoSplat fuses complementary geometry cues via a cost-volume branch and a triplane-based 3D volume branch and leverages continuous pose encoding to generalize across view counts and camera configurations. Second, since multiple posed stereo pairs are typically unavailable at inference time, we introduce a diffusion-enhanced one-shot progressive inference scheme called StereoSplat+: starting from one stereo pair, we render novel stereo views from the predicted 3DGS, refine them with a one-step diffusion enhancer, and feed them back as additional inputs to update the 3DGS. Experiments on the KITTI-360 dataset show that StereoSplat+ improves novel-view rendering quality and geometry accuracy, especially in occluded regions and under strong view extrapolation, outperforming recent feed-forward 3DGS baselines.
