Robots typically possess sensors of different modalities, such as colour cameras, inertial measurement units, and 3D laser scanners. Often, solving a particular problem becomes easier when more than one modality is used. However, while there are undeniable benefits to combine sensors of different modalities the process tends to be complicated. Segmenting scenes observed by the robot into a discrete set of classes is a central requirement for autonomy as understanding the scene is the first step to reason about future situations. Scene segmentation is commonly performed using either image data or 3D point cloud data. In computer vision many successful methods for scene segmentation are based on conditional random fields (CRF) where the maximum a posteriori (MAP) solution to the segmentation can be obtained by inference. In this paper we devise a new CRF inference method for scene segmentation that incorporates global constraints, enforcing the sets of nodes are assigned the same class label. To do this efficiently, the CRF is formulated as a relaxed quadratic program whose MAP solution is found using a gradient-based optimisation approach. The proposed method is evaluated on images and 3D point cloud data gathered in urban environments where image data provides the appearance features needed by the CRF, while the 3D point cloud data provides global spatial constraints over sets of nodes. Comparisons with belief propagation, conventional quadratic programming relaxation, and higher order potential CRF show the benefits of the proposed method.
HGS-Mapping: Online Dense Mapping Using Hybrid Gaussian Representation in Urban Scenes
Fudan University（复旦大学）；School of Future Technology, Harbin Institute of Technology（哈尔滨工业大学未来技术学院）；Department of Computer Science and Engineering, Shanghai Jiao Tong University（上海交通大学计算机科学与工程系）
Grid-guided Neural Radiance Fields for Large Urban Scenes
Zhejiang University（浙江大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Adobe Research（奥多比研究中心）；Shanghai AI Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）
UrbanGS: A Scalable and Efficient Architecture for Geometrically Accurate Large-Scene Reconstruction
Hangzhou International Innovation Institute, Beihang University（北京航空航天大学杭州国际创新研究院）；Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）；School of Software, Beihang University（北京航空航天大学软件学院）；State Key Laboratory of Media Convergence and Communication, Communication University of China（中国传媒大学媒体融合与传播国家重点实验室）；Department of Computer Science, National University of Singapore（新加坡国立大学计算机科学系）；Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）；Artificial Intelligence Research Center, Lobachevsky State University（洛夫奇夫斯基国立大学人工智能研究中心）
3D Gaussian Splatting for Fine-grained Urban Scene Reconstruction via Semantic-guided Density Control and Loss Design
School of Computer Science, China University of Geosciences (Wuhan)（中国地质大学（武汉）计算机学院）；School of Architecture and Urban Planning, Shenzhen University（深圳大学建筑与城市规划学院）；Guangdong Key Laboratory of Urban Informatics, Shenzhen University（深圳大学广东省城市信息学重点实验室）；MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Shenzhen University（深圳大学自然资源部大湾区地理环境监测重点实验室）
VAD-GS: Visibility-Aware Densification for 3D Gaussian Splatting in Dynamic Urban Scenes
Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University（同济大学智能自主系统研究所）；College of Electronic and Information Engineering, Tongji University（同济大学电子与信息工程学院）；National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Xi’an Jiaotong University（西安交通大学人机混合增强智能国家实验室）
Gap Completion in Point Cloud Scene occluded by Vehicles using SGC-Net
Chair of Cartography and Visual Analytics, Technical University of Munich（慕尼黑工业大学制图与视觉分析主席）；Institute of Cartography and Geoinformatics, Leibniz University Hannover（汉诺威莱布尼茨大学制图与地理信息研究所）；Computer Vision Group, Technical University of Munich（慕尼黑工业大学计算机视觉组）
L2RSI: Cross-view LiDAR-based Place Recognition for Large-scale Urban Scenes via Remote Sensing Imagery
Fujian Key Laboratory of Sensing and Computing for Smart Cities, Xiamen University, China（福建智能城市感知与计算重点实验室，厦门大学，中国）；Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, China（多媒体可信感知与高效计算重点实验室，中华人民共和国教育部，厦门大学，中国）；University of Science and Technology of China, China（中国科学技术大学，中国）
SparseGF: A Height-Aware Sparse Segmentation Framework with Context Compression for Robust Ground Filtering Across Urban to Natural Scenes
School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology（南京信息工程大学遥感与地理信息工程学院）；Tiandu-Nuist Deep Space Exploration Laboratory（天都-南京信息工程大学深空探测实验室）；School of Remote Sensing and Information Engineering, Wuhan University（武汉大学遥感与信息工程学院）；School of Land Science and Technology, China University of Geosciences（中国地质大学（北京）土地科学与技术学院）；School of Geospatial Artificial Intelligence, East China Normal University（华东师范大学地理信息智能学院）；Department of Geography and Environmental Management, University of Waterloo（滑铁卢大学地理与环境管理系）；Department of Systems Design Engineering, University of Waterloo（滑铁卢大学系统设计工程系）
SPORTS: Simultaneous Panoptic Odometry, Rendering, Tracking and Segmentation for Urban Scenes Understanding
School of Information Science and Engineering, Yunnan University（云南大学信息科学与工程学院）；Yunnan University（云南大学）；School of Information and Electronics, Beijing Institute of Technology（北京理工大学信息与电子学院）；Yunnan Key Laboratory of Intelligent Systems and Computing（智能系统与计算重点实验室）
WHU-PCPR: A cross-platform heterogeneous point cloud dataset for place recognition in complex urban scenes
School of Advanced Manufacturing, Nanchang University（南昌大学先进制造学院）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电子与电气工程学院）；Department of Geomatics Engineering, University of Calgary（卡尔加里大学测绘工程系）；School of Earth Sciences and Engineering, Hohai University（河海大学地球科学与工程学院）；School of Geography and Environment, Jiangxi Normal University（江西师范大学地理与环境学院）；School of Information Engineering, Nanchang University（南昌大学信息工程学院）；State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）；Hubei Luojia Laboratory（湖北省珞珈实验室）
