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
NOPE: Novel Object Pose Estimation from a Single Image
Van Nguyen Nguyen, Thibault Groueix, Yinlin Hu, Mathieu Salzmann, Vincent Lepetit
NS3D: Neuro-Symbolic Grounding of 3D Objects and Relations
Joy Hsu, Jiayuan Mao, Jiajun Wu
6D Object Pose Estimation from Approximate 3D Models for Orbital Robotics
Maximilian Ulmer, Maximilian Durner, Martin Sundermeyer, Manuel Stoiber, Rudolph Triebel