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
Fixing Malfunctional Objects With Learned Physical Simulation and Functional Prediction
Yining Hong, Kaichun Mo, Li Yi, Leonidas J. Guibas, Antonio Torralba, Joshua B. Tenenbaum, Chuang Gan
The Batch Artifact Scanning Protocol: A new method using computed tomography (CT) to rapidly create three-dimensional models of objects from large collections en masse
Katrina Yezzi-Woodley, Jeff Calder, Mckenzie Sweno, Chloe Siewert, Peter J. Olver