3D Data
3D data analysis focuses on extracting meaningful information from three-dimensional representations of the world, encompassing diverse applications from medical imaging to cultural heritage preservation. Current research emphasizes efficient data processing and representation, leveraging techniques like neural fields, graph representations, and deep learning architectures (including convolutional neural networks and transformers) to address challenges in segmentation, object detection, and scene understanding. These advancements are driving improvements in areas such as medical diagnosis, autonomous navigation, and the preservation of digital cultural artifacts, highlighting the growing importance of robust and scalable 3D data processing methods.
Papers
Automatically Annotating Indoor Images with CAD Models via RGB-D Scans
Stefan Ainetter, Sinisa Stekovic, Friedrich Fraundorfer, Vincent Lepetit
Depth Estimation maps of lidar and stereo images
Fei Wu, Luoyu Chen
Supervised Anomaly Detection Method Combining Generative Adversarial Networks and Three-Dimensional Data in Vehicle Inspections
Yohei Baba, Takuro Hoshi, Ryosuke Mori, Gaurang Gavai