Large Scale 3D
Large-scale 3D processing focuses on efficiently and accurately analyzing and manipulating vast amounts of three-dimensional data. Current research emphasizes developing novel model architectures, such as transformers and diffusion models, to improve tasks like 3D object detection, segmentation, generation, and scene understanding, often leveraging large-scale datasets and incorporating techniques like contrastive learning and implicit field representations. These advancements are crucial for various applications, including robotics, autonomous driving, and virtual/augmented reality, by enabling more realistic and efficient simulations and interactions with complex 3D environments.
Papers
LargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous Driving
Lingdong Kong, Xiang Xu, Youquan Liu, Jun Cen, Runnan Chen, Wenwei Zhang, Liang Pan, Kai Chen, Ziwei Liu
Advancing the Understanding of Fine-Grained 3D Forest Structures using Digital Cousins and Simulation-to-Reality: Methods and Datasets
Jing Liu, Duanchu Wang, Haoran Gong, Chongyu Wang, Jihua Zhu, Di Wang
SceneFactor: Factored Latent 3D Diffusion for Controllable 3D Scene Generation
Alexey Bokhovkin, Quan Meng, Shubham Tulsiani, Angela Dai
Structured 3D Latents for Scalable and Versatile 3D Generation
Jianfeng Xiang, Zelong Lv, Sicheng Xu, Yu Deng, Ruicheng Wang, Bowen Zhang, Dong Chen, Xin Tong, Jiaolong Yang