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
iDet3D: Towards Efficient Interactive Object Detection for LiDAR Point Clouds
Dongmin Choi, Wonwoo Cho, Kangyeol Kim, Jaegul Choo
Hyper-VolTran: Fast and Generalizable One-Shot Image to 3D Object Structure via HyperNetworks
Christian Simon, Sen He, Juan-Manuel Perez-Rua, Mengmeng Xu, Amine Benhalloum, Tao Xiang