Multi-View Consistency

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10 papers in the last four weeks, up 43% on the four weeks before. 0.1% of all new papers.

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SpatialMosaic: A Multiview VLM Dataset for Partial Visibility
Dec 29, 2025Kanghee Lee, Jungi Hong, Sion Lee +4Multi-View Consistency Occlusion Reasoning
Seoul National University · Kyung Hee University · University College London +1
Multi-View Foundation Models
Dec 17, 2025Leo Segre, Or Hirschorn, Shai AvidanMulti-View Consistency Vision Foundation Model Adaptation
Tel Aviv University
3D-Consistent Multi-View Editing by Correspondence Guidance
Nov 27, 2025Josef Bengtson, David Nilsson, Dong In Lee +2Multi-View Consistency Diffusion Model Guidance
Chalmers University of Technology · Korea University
VolSplat: Rethinking Feed-Forward 3D Gaussian Splatting with Voxel-Aligned Prediction
Sep 23, 2025Weijie Wang, Yeqing Chen, Zeyu Zhang +9Multi-View Consistency 3D Gaussian Splatting
Zhejiang University · GigaAI · The Chinese University of Hong Kong +4
MAGIC: Learning from Visibility Asymmetry for Unsupervised Stereo Matching
Aug 14, 2025Peng Xu, Zhiyu XiangStereo Matching Occlusion Reasoning
College of Information Science and Electronic Engineering (ISEE) Zhejiang University, China
DisCo3D: Distilling Multi-View Consistency for 3D Scene Editing
Aug 3, 2025Yufeng Chi, Huimin Ma, Kafeng Wang +1Multi-View Consistency 3D Scene Editing
Institute for Artificial Intelligence, Beijing National Research Center for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China · College of Computer, National University of Defense Technology, Changsha 410073, China
ArtNVG: Content-Style Separated Artistic Neighboring-View Gaussian Stylization
Dec 25, 2024Zixiao Gu, Mengtian Li, Ruhua Chen +5Multi-View Consistency 3D Gaussian Splatting
Fudan University · Shanghai University · Shanghai Theatre Academy
MVGS: Multi-view Regulated Gaussian Splatting for Novel View Synthesis
Oct 2, 2024Xiaobiao Du, Yida Wang, Xin YuMulti-View Consistency 3D Gaussian Splatting
University of Technology Sydney · Li Auto Inc. · Australian Institute for Machine Learning, Adelaide University
AnyView: Synthesizing Any Novel View in Dynamic Scenes
Date pendingBasile Van Hoorick, Dian Chen, Shun Iwase +7Multi-View Consistency Camera-Conditioned Video Generation
Toyota Research Institute, CA, USA · Amazon Web Services, CA, USA

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SpatialMosaic: A Multiview VLM Dataset for Partial Visibility

Kanghee Lee, Jungi Hong, Sion Lee, Injae Lee+3

Seoul National University · Kyung Hee University · University College London+1

Multi-View Consistency Occlusion Reasoning 3D Spatial Reasoning Multimodal QA 3D Scene Understanding

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Recent progress in Multimodal Large Language Models (MLLMs) has enabled 3D scene understanding and spatial reasoning directly from multi-view images, without requiring explicit 3D reconstructions. Nevertheless, key challenges that frequently arise in real-world environments, such as partial visibility, occlusion, and low-overlap conditions that require reasoning from fragmented visual cues, remain under-explored. To address these limitations, we propose a scalable multi-view data generation and annotation pipeline that constructs realistic spatial reasoning QAs, resulting in SpatialMosaic, a comprehensive instruction-tuning dataset with 2M QA pairs. We further introduce SpatialMosaic-Bench, a challenging benchmark for evaluating multi-view spatial reasoning under complex and diverse scenarios, consisting of 1M QA pairs across 11 tasks with both multiple-choice and numerical-answer formats. Our dataset spans both indoor and outdoor scenes, enabling comprehensive evaluation across diverse real-world scenarios. In addition, we provide a practical baseline for multi-view settings by integrating geometry encoders into VLMs for improved cross-view consistency and spatial grounding. Extensive experiments demonstrate that our dataset effectively enhances spatial reasoning under challenging multi-view conditions, validating the effectiveness of our data generation pipeline in constructing realistic and challenging QAs.

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