3D Object
3D object modeling research focuses on accurately and efficiently representing three-dimensional objects from various data sources, including images, point clouds, and sensor data, with primary objectives of reconstruction, manipulation, and understanding. Current research emphasizes the development of novel algorithms and architectures, such as diffusion models, Gaussian splatting, and transformers, to improve the accuracy, efficiency, and generalization capabilities of 3D models, often incorporating multi-view information and physical constraints. These advancements have significant implications for diverse fields, including autonomous driving, robotics, virtual and augmented reality, and medical imaging, by enabling more realistic simulations, improved object recognition, and enhanced human-computer interaction.
Papers
Exploring Domain Shift on Radar-Based 3D Object Detection Amidst Diverse Environmental Conditions
Miao Zhang, Sherif Abdulatif, Benedikt Loesch, Marco Altmann, Marius Schwarz, Bin Yang
DC3DO: Diffusion Classifier for 3D Objects
Nursena Koprucu, Meher Shashwat Nigam, Shicheng Xu, Biruk Abere, Gabriele Dominici, Andrew Rodriguez, Sharvaree Vadgam, Berfin Inal, Alberto Tono