We present UrbanScene3D, a large-scale data platform for research of urban scene perception and reconstruction. UrbanScene3D contains over 128k high-resolution images covering 16 scenes including large-scale real urban regions and synthetic cities with 136 km^2 area in total. The dataset also contains high-precision LiDAR scans and hundreds of image sets with different observation patterns, which provide a comprehensive benchmark to design and evaluate aerial path planning and 3D reconstruction algorithms. In addition, the dataset, which is built on Unreal Engine and Airsim simulator together with the manually annotated unique instance label for each building in the dataset, enables the generation of all kinds of data, e.g., 2D depth maps, 2D/3D bounding boxes, and 3D point cloud/mesh segmentations, etc. The simulator with physical engine and lighting system not only produce variety of data but also enable users to simulate cars or drones in the proposed urban environment for future research.
MTFusion: Reconstructing Any 3D Object from Single Image Using Multi-word Textual Inversion
National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University（四川大学合成视觉基础科学国家重点实验室）；College of Computer Science, Sichuan University（四川大学计算机学院）
Capturing Shape Information with Multi-Scale Topological Loss Terms for 3D Reconstruction
Institute of AI for Health, Helmholtz Munich – German Research Centre for Environmental Health（亥姆霍兹慕尼黑中心（德国环境健康研究中心）健康人工智能研究所）；Technical University of Munich（慕尼黑工业大学）
Capturing Dynamic Textured Surfaces of Moving Targets
University of Southern California（南加州大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Toyota Technological Institute at Chicago（芝加哥丰田技术研究院）；Adobe Research（奥多比研究院）
Simulating Distribution Dynamics: Liquid Temporal Feature Evolution for Single-Domain Generalized Object Detection
College of Intelligence and Computing, Tianjin University（智能与计算学院，天津大学）；School of Computer Science and Information Engineering, Hefei University of Technology（计算机科学与信息工程学院，合肥工业大学）
