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PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm
Abstract: In contrast to numerous NLP and 2D computer vision foundational models, the learning of a robust and highly generalized 3D foundational model poses considerably greater challenges. This is primarily due to the inherent data variability and the diversity of downstream tasks.
Haoyi Zhu 朱皓怡, Honghui Yang, Xiaoyang Wu, Di Huang, Sha Zhang, Xianglong He, Tong He, Hengshuang Zhao, Chunhua Shen, Yu Qiao, Wanli Ouyang
Cite Github PDF Arxiv
PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm
UniPAD: A Universal Pre-training Paradigm for Autonomous Driving
Abstract: In the context of autonomous driving, the significance of effective feature learning is widely acknowledged. While conventional 3D self-supervised pre-training methods have shown widespread success, most methods follow the ideas originally designed for 2D images.
Honghui Yang, Sha Zhang, Di Huang, Xiaoyang Wu, Haoyi Zhu 朱皓怡, Tong He, Shixiang Tang, Hengshuang Zhao, Qibo Qiu, Binbin Lin, Xiaofei He, Wanli Ouyang
Cite Github PDF Arxiv
UniPAD: A Universal Pre-training Paradigm for Autonomous Driving