Sparse-View 3D Reconstruction

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Sparse-view 3D reconstruction studies how to infer a coherent 3D scene from only a few images, enabling novel-view rendering, measurement, and simulation when dense capture is impractical. Methods optimize neural radiance fields or 3D Gaussian splats per scene, or use feed-forward models that predict geometry, camera poses, and appearance directly. Depth and stereo cues, pretrained 3D foundation models, uncertainty estimates, and diffusion-generated views are increasingly used to constrain poorly observed regions and guide view selection. The central challenge is completing unseen areas without letting inaccurate priors or generated details contradict the evidence, while balancing geometric accuracy, visual quality, generalization, and speed.

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188 papers24 papers in the last four weeks, up 4% on the four weeks before. 0.1% of all new papers.

GAINS: Gaussian-based Inverse Rendering from Sparse Multi-View Captures
Dec 10, 2025Patrick Noras, Jun Myeong Choi, Didier Stricker +2Inverse Rendering 3D Gaussian Splatting
University of Kaiserslautern-Landau, Kaiserslautern, Germany · German Research Center for Artificial Intelligence, Kaiserslautern, Germany · University of North Carolina at Chapel Hill, NC, USA +1
Gaussians on Fire: High-Frequency Reconstruction of Flames
Nov 27, 2025Jakob Nazarenus, Dominik Michels, Wojtek Palubicki +43D Gaussian Splatting Dynamic Scene Reconstruction
Kiel University Germany · KAUST, CEMSE Saudi Arabia · Adam Mickiewicz University Poland +1
SPFSplatV2: Efficient Self-Supervised Pose-Free 3D Gaussian Splatting from Sparse Views
Sep 21, 2025Ranran Huang, Krystian Mikolajczyk3D Gaussian Splatting 3D Reconstruction
Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, United Kingdom
End-to-End 4D Heart Mesh Recovery Across Full-Stack and Sparse Cardiac MRI
Sep 15, 2025Yihong Chen, Jiancheng Yang, Deniz Sayin Mercadier +34D Reconstruction 3D Reconstruction
School of Computer and Communication Science, EPFL · ELLIS Institute Finland, Aalto University, Finland · University of North Carolina at Charlotte +1
RUSplatting: Robust 3D Gaussian Splatting for Sparse-View Underwater Scene Reconstruction
May 21, 2025Zhuodong Jiang, Haoran Wang, Guoxi Huang +2Underwater 3D Reconstruction 3D Gaussian Splatting
Visual Information Laboratory, University of Bristol, Bristol, UK · Submerged Resources Center, National Park Service, Denver, CO, USA
SparseLGS: Sparse View Language Embedded Gaussian Splatting
Dec 3, 2024Jun Hu, Zhang Chen, Zhong Li +23D Gaussian Splatting Language-Embedded 3DGS
School of Mathematical Science, University of Science and Technology of China · Meta · Apple Inc. +1
3D Vessel Reconstruction from Sparse-View Dynamic DSA Images via Vessel Probability Guided Attenuation Learning
May 17, 2024Zhentao Liu, Huangxuan Zhao, Wenhui Qin +7Medical Imaging Neural Rendering
School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China · National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan, China · School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China +1
SparseGS: Sparse View Synthesis using 3D Gaussian Splatting
Nov 30, 2023Haolin Xiong, Sairisheek Muttukuru, Hanyuan Xiao +43D Gaussian Splatting Novel View Synthesis
University of California, Los Angeles · USC Institute for Creative Technologies · University of Southern California

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GAINS: Gaussian-based Inverse Rendering from Sparse Multi-View Captures

Patrick Noras, Jun Myeong Choi, Didier Stricker, Pieter Peers+1

University of Kaiserslautern-Landau, Kaiserslautern, Germany · German Research Center for Artificial Intelligence, Kaiserslautern, Germany · University of North Carolina at Chapel Hill, NC, USA+1

Inverse Rendering 3D Gaussian Splatting Sparse-View 3D Reconstruction Physically Based Rendering

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Recent advances in Gaussian Splatting-based inverse rendering extend Gaussian primitives with shading parameters and physically grounded light transport, enabling high-quality material recovery from dense multi-view captures. However, the accuracy of these methods degrades under sparse-view settings, where limited observations lead to severe ambiguity between geometry, reflectance, and lighting. We introduce GAINS (Gaussian-based Inverse rendering from Sparse multi-view captures), a two-stage inverse rendering framework that leverages foundation models as priors to stabilize geometry and material estimation. The core technical contribution of this paper is an inverse rendering framework that unifies foundation model priors with physically-based representations in an optimization scheme. GAINS first refines geometry using monocular depth, normal, and diffusion priors, and then employs segmentation, intrinsic image decomposition (IID), and diffusion priors to regularize material recovery. Extensive experiments on synthetic and real-world datasets show that GAINS significantly improves material parameter accuracy, relighting quality, and novel-view synthesis compared to state-of-the-art Gaussian-based inverse rendering methods. While GAINS outperforms and remains competitive across a wide range of objects captured with 4 to 32 cameras, the improvement is particularly pronounced under sparse-view settings, where ambiguity is high and learning-based priors become especially beneficial. Project page: https://patrickbail.github.io/gains/