3D Gaussian Splatting (GS) significantly struggles to accurately represent the underlying 3D scene geometry, resulting in inaccuracies and floating artifacts when rendering depth maps. In this paper, we address this limitation, undertaking a comprehensive analysis of the integration of depth priors throughout the optimization process of Gaussian primitives, and present a novel strategy for this purpose. This latter dynamically exploits depth cues from a readily available stereo network, processing virtual stereo pairs rendered by the GS model itself during training and achieving consistent self-improvement of the scene representation. Experimental results on three popular datasets, breaking ground as the first to assess depth accuracy for these models, validate our findings.
Stereo-GS: Multi-View Stereo Vision Model for Generalizable 3D Gaussian Splatting Reconstruction
Department of Computer Science, Hong Kong Baptist University（香港 Baptist 大学计算机科学系）；NVIDIA AI Technology Center, NVIDIA（NVIDIA 人工智能技术中心）；School of Control Science and Engineering, Shandong University（控制科学与工程学院，山东大学）
Learning Fine-Grained Geometry for Sparse-View Splatting via Cascade Depth Loss
School of Computer Science The University of Sydney Sydney, Australia；School of Computer Science University of Bristol Bristol, United Kingdom；School of Science Edith Cowan University Perth, Australia
3D Gaussian Splatting aided Localization for Large and Complex Indoor-Environments
Institute for Photogrammetry and Geoinformatics, University of Stuttgart, Germany（摄影测量与地理信息学研究所，斯图加特大学，德国）；Institute of Geomatics, University of Applied Sciences and Arts Northwestern Switzerland（地理信息研究所，北瑞士应用科学与艺术大学）
MVSGaussian: Fast Generalizable Gaussian Splatting Reconstruction from Multi-View Stereo
Great Bay University（湾谷大学）；Huazhong University of Science and Technology（华中科技大学）；Nanyang Technological University（南洋理工大学）；Shanghai AI Laboratory（上海人工智能实验室）
FreGS: 3D Gaussian Splatting with Progressive Frequency Regularization
Nanyang Technological University（南洋理工大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Carnegie Mellon University（卡内基梅隆大学）；MBZUAI（穆罕默德·本·扎耶德人工智能大学）
DualDiff3D: Dual Structure-Appearance Diffusion Priors for Reliability-Enhanced 3D Gaussian Splatting
School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；Pengcheng Laboratory（鹏城实验室）；Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology（广东省超高清沉浸式媒体技术重点实验室）
National University of Singapore（新加坡国立大学）；DSO National Laboratories（新加坡国防科技研究院）；Yale-NUS College（耶鲁-新加坡国立大学学院）
Nighttime stereo depth estimation is still challenging, as assumptions associated with daytime lighting conditions do not hold any longer. Nighttime is not only about low-light and dense noise, but also about glow/glare, flares, non-uniform distribution of light, etc. One of the possible solutions is to train a network on night stereo images in a fully supervised manner. However, to obtain proper disparity ground-truths that are dense, independent from glare/glow, and have sufficiently far depth ranges is extremely intractable. To address the problem, we introduce a network joining day/night t…
University of Liverpool（利物浦大学）；Xi’an Jiaotong-Liverpool University（西交利物浦大学）；Universidad Antonio de Nebrija（安东尼奥·德·内布里哈大学）；ARIES Research Centre（ARIES研究中心）
Gaussian Splatting Feature Fields for Privacy-Preserving Visual Localization
Faculty of Electrical Engineering, Czech Technical University in Prague（1 电气工程学院，捷克技术大学普拉赫堡分校）；Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague（2 捷克信息学、机器人学与自动化研究所，捷克技术大学普拉赫堡分校）；NAVER LABS Europe（3 NAVER LABS欧洲）
