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
Real-time Neural Radiance Talking Portrait Synthesis via Audio-spatial Decomposition
Jiaxiang Tang, Kaisiyuan Wang, Hang Zhou, Xiaokang Chen, Dongliang He, Tianshu Hu, Jingtuo Liu, Gang Zeng, Jingdong Wang
ONeRF: Unsupervised 3D Object Segmentation from Multiple Views
Shengnan Liang, Yichen Liu, Shangzhe Wu, Yu-Wing Tai, Chi-Keung Tang
Transformation-Equivariant 3D Object Detection for Autonomous Driving
Hai Wu, Chenglu Wen, Wei Li, Xin Li, Ruigang Yang, Cheng Wang
Multi-Camera Multi-Object Tracking on the Move via Single-Stage Global Association Approach
Pha Nguyen, Kha Gia Quach, Chi Nhan Duong, Son Lam Phung, Ngan Le, Khoa Luu
Language Conditioned Spatial Relation Reasoning for 3D Object Grounding
Shizhe Chen, Pierre-Louis Guhur, Makarand Tapaswi, Cordelia Schmid, Ivan Laptev