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
Eliminating Cross-modal Conflicts in BEV Space for LiDAR-Camera 3D Object Detection
Jiahui Fu, Chen Gao, Zitian Wang, Lirong Yang, Xiaofei Wang, Beipeng Mu, Si Liu
SparseLIF: High-Performance Sparse LiDAR-Camera Fusion for 3D Object Detection
Hongcheng Zhang, Liu Liang, Pengxin Zeng, Xiao Song, Zhe Wang
GaussianObject: High-Quality 3D Object Reconstruction from Four Views with Gaussian Splatting
Chen Yang, Sikuang Li, Jiemin Fang, Ruofan Liang, Lingxi Xie, Xiaopeng Zhang, Wei Shen, Qi Tian
MM-Point: Multi-View Information-Enhanced Multi-Modal Self-Supervised 3D Point Cloud Understanding
Hai-Tao Yu, Mofei Song