1 code implementation • 14 Mar 2024 • Yinan Deng, Jiahui Wang, Jingyu Zhao, Xinyu Tian, Guangyan Chen, Yi Yang, Yufeng Yue
Environment maps endowed with sophisticated semantics are pivotal for facilitating seamless interaction between robots and humans, enabling them to effectively carry out various tasks.
no code implementations • 14 Sep 2023 • Yu Gao, Lutong Su, Hao Liang, Yufeng Yue, Yi Yang, Mengyin Fu
In this paper, we propose MC-NeRF, a method that enables joint optimization of both intrinsic and extrinsic parameters alongside NeRF.
1 code implementation • IEEE Intelligent Vehicles Symposium (IV) 2023 • Jun Zhang∗, Yiyao Liu∗, Mingxing Wen, Yufeng Yue, Haoyuan Zhang and Danwei Wang
To unify the process, an important step is to automatically and robustly detect the target from different types of LiDARs.
1 code implementation • NeurIPS 2023 • Guangyan Chen, Meiling Wang, Yi Yang, Kai Yu, Li Yuan, Yufeng Yue
Large language models (LLMs) based on the generative pre-training transformer (GPT) have demonstrated remarkable effectiveness across a diverse range of downstream tasks.
no code implementations • 26 Mar 2023 • Xihan Wang, Xi Xu, Yu Gao, Yi Yang, Yufeng Yue, Mengyin Fu
Compared with the previous work for muti-point representation, the experiments show that CRRS can improve the training performance both in accurate and stability.
no code implementations • 17 Jan 2023 • Yu Gao, Xi Xu, Tianji Jiang, Siyuan Chen, Yi Yang, Yufeng Yue, Mengyin Fu
For example, 2D object detection usually requires a large amount of 2D annotation data with high cost.
1 code implementation • ICCV 2023 • Guangyan Chen, Meiling Wang, Li Yuan, Yi Yang, Yufeng Yue
In this paper, a critical observation is made that the invisible parts of each point cloud can be directly utilized as inherent masks, and the aligned point cloud pair can be regarded as the reconstruction target.
1 code implementation • 17 Dec 2021 • Guangyan Chen, Meiling Wang, Yufeng Yue, Qingxiang Zhang, Li Yuan
Recent Transformer-based methods have achieved advanced performance in point cloud registration by utilizing advantages of the Transformer in order-invariance and modeling dependency to aggregate information.
no code implementations • 19 Aug 2021 • Guohao Peng, Yufeng Yue, Jun Zhang, Zhenyu Wu, Xiaoyu Tang, Danwei Wang
(2) By exploiting the interpretability of the local weighting scheme, a semantic constrained initialization is proposed so that the local attention can be reinforced by semantic priors.