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FSGS: Real-Time Few-shot View Synthesis using Gaussian Splatting
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Novel view synthesis from limited observations remains an important and persistent task. However, high efficiency in existing NeRF-based few-shot view synthesis is often compromised to obtain an accurate 3D representation. To address this challenge, we propose a few-shot view synthesis framework based on 3D Gaussian Splatting that enables real-time and photo-realistic view synthesis with as few as three training views. The proposed method, dubbed FSGS, handles the extremely sparse initialized SfM points with a thoughtfully designed Gaussian Unpooling process. Our method iteratively distributes new Gaussians around the most representative locations, subsequently infilling local details in vacant areas. We also integrate a large-scale pre-trained monocular depth estimator within the Gaussians optimization process, leveraging online augmented views to guide the geometric optimization towards an optimal solution. Starting from sparse points observed from limited input viewpoints, our FSGS can accurately grow into unseen regions, comprehensively covering the scene and boosting the rendering quality of novel views. Overall, FSGS achieves state-of-the-art performance in both accuracy and rendering efficiency across diverse datasets, including LLFF, Mip-NeRF360, and Blender. Project website: https://zehaozhu.github.io/FSGS/.
Forward citations
Cited by 11 Pith papers
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PointGS: Point Attention-Aware Sparse View Synthesis with Gaussian Splatting
PointGS improves few-shot 3D Gaussian splatting by fusing multi-view image features per 3D point and refining them with a neighbor-attention network before decoding Gaussian colors.
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MonoSplat: Generalizable 3D Gaussian Splatting from Monocular Depth Foundation Models
A feed-forward architecture that reuses a frozen depth foundation model to predict 3D Gaussian primitives, improving novel view synthesis and cross-dataset generalization.
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RDG-GS: Relative Depth Guidance with Gaussian Splatting for Real-time Sparse-View 3D Rendering
RDG-GS combines refined monocular depth priors, a relative depth similarity loss, and adaptive point densification to improve sparse-view 3D Gaussian Splatting rendering.
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EasySplat: View-Adaptive Learning makes 3D Gaussian Splatting Easy
EasySplat combines DUSt3R pointmap initialization with a KNN-based Gaussian splitting rule to improve 3D Gaussian Splatting for dense-view novel view synthesis.
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SLGaussian: Fast Language Gaussian Splatting in Sparse Views
SLGaussian builds a 3D semantic field from two photos in a single forward pass, stores CLIP features in a memory bank for fast open-vocabulary queries, and reports higher IoU than LangSplat and LERF on the LERF and 3D...
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4D Gaussian Splatting with Scale-aware Residual Field and Adaptive Optimization for Real-time Rendering of Temporally Complex Dynamic Scenes
SaRO-GS models dynamic scenes with 4D Gaussians plus a scale-aware residual field and adaptive per-Gaussian optimization, achieving state-of-the-art PSNR at real-time frame rates on D-NeRF and Plenoptic Video datasets.
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DGTR: Distributed Gaussian Turbo-Reconstruction for Sparse-View Vast Scenes
A distributed pipeline using a pretrained feed-forward Gaussian model, global alignment, depth regularization, and distillation-based merging reconstructs sparse-view large-scale aerial scenes faster than prior methods.
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Unveiling Trust in Multimodal Large Language Models: Evaluation, Analysis, and Mitigation
MultiTrust-X is a new 32-task, 28-dataset benchmark over 30 multimodal LLMs claiming that trustworthiness lags capability, that multimodality amplifies base-model risks, and that its RESA alignment method reaches stat...
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SuperGS: Consistent and Detailed 3D Super-Resolution Scene Reconstruction via Gaussian Splatting
SuperGS outperforms prior Gaussian-splatting methods on high-resolution novel view synthesis by combining a latent feature field, multi-view voting densification, and variational uncertainty weighting.
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FatesGS: Fast and Accurate Sparse-View Surface Reconstruction using Gaussian Splatting with Depth-Feature Consistency
FatesGS combines local monocular depth ranking, depth smoothing, and multi-view feature alignment in a 2D Gaussian splatting pipeline to obtain accurate surface meshes from only three views without dataset-scale pre-training.
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Sparse Input View Synthesis: 3D Representations and Reliable Priors
Regularizing sparse-input radiance fields with visibility priors, simpler-solution depth supervision, and sparse flow priors improves novel view synthesis and depth estimation on multiple benchmarks.
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