Zobrazeno 1 - 10
of 6 884
pro vyhledávání: '"Siyan A"'
This paper explores the transmission schemes for multi-channel water-to-air optical wireless communication (W2A-OWC) and introduces a prototype of a real-time W2A-OWC system based on a field-programmable gate array (FPGA). Utilizing an LED array as t
Externí odkaz:
http://arxiv.org/abs/2411.11866
Autor:
Zhenyuan, Yang, Zhengliang, Liu, Jing, Zhang, Cen, Lu, Jiaxin, Tai, Tianyang, Zhong, Yiwei, Li, Siyan, Zhao, Teng, Yao, Qing, Liu, Jinlin, Yang, Qixin, Liu, Zhaowei, Li, Kexin, Wang, Longjun, Ma, Dajiang, Zhu, Yudan, Ren, Bao, Ge, Wei, Zhang, Ning, Qiang, Tuo, Zhang, Tianming, Liu
This study examines the capabilities of advanced Large Language Models (LLMs), particularly the o1 model, in the context of literary analysis. The outputs of these models are compared directly to those produced by graduate-level human participants. B
Externí odkaz:
http://arxiv.org/abs/2410.18142
Users can divulge sensitive information to proprietary LLM providers, raising significant privacy concerns. While open-source models, hosted locally on the user's machine, alleviate some concerns, models that users can host locally are often less cap
Externí odkaz:
http://arxiv.org/abs/2410.17127
Autor:
Jiang, Eric Hanchen, Zhang, Zhi, Zhang, Dinghuai, Lizarraga, Andrew, Xu, Chenheng, Zhang, Yasi, Zhao, Siyan, Xu, Zhengjie, Yu, Peiyu, Tang, Yuer, Kong, Deqian, Wu, Ying Nian
Advancements in reinforcement learning have led to the development of sophisticated models capable of learning complex decision-making tasks. However, efficiently integrating world models with decision transformers remains a challenge. In this paper,
Externí odkaz:
http://arxiv.org/abs/2410.11359
Autor:
Wang, Siyan, Levy, Bradford
Many of the recent breakthroughs in language modeling have resulted from scaling effectively the same model architecture to larger datasets. In this vein, recent work has highlighted performance gains from increasing training dataset size and quality
Externí odkaz:
http://arxiv.org/abs/2409.17827
Autor:
Liao, Zimu, Chen, Siyan, Fu, Rong, Wang, Yi, Su, Zhongling, Luo, Hao, Ma, Li, Xu, Linning, Dai, Bo, Li, Hengjie, Pei, Zhilin, Zhang, Xingcheng
Recently, 3D Gaussian Splatting (3DGS) has garnered attention for its high fidelity and real-time rendering. However, adapting 3DGS to different camera models, particularly fisheye lenses, poses challenges due to the unique 3D to 2D projection calcul
Externí odkaz:
http://arxiv.org/abs/2409.04751
Autor:
Liu, Changkun, Chen, Shuai, Bhalgat, Yash, Hu, Siyan, Cheng, Ming, Wang, Zirui, Prisacariu, Victor Adrian, Braud, Tristan
We leverage 3D Gaussian Splatting (3DGS) as a scene representation and propose a novel test-time camera pose refinement framework, GSLoc. This framework enhances the localization accuracy of state-of-the-art absolute pose regression and scene coordin
Externí odkaz:
http://arxiv.org/abs/2408.11085
Autor:
Feng, Guofeng, Chen, Siyan, Fu, Rong, Liao, Zimu, Wang, Yi, Liu, Tao, Pei, Zhilin, Li, Hengjie, Zhang, Xingcheng, Dai, Bo
This work introduces FlashGS, an open-source CUDA Python library, designed to facilitate the efficient differentiable rasterization of 3D Gaussian Splatting through algorithmic and kernel-level optimizations. FlashGS is developed based on the observa
Externí odkaz:
http://arxiv.org/abs/2408.07967
3D surface reconstruction from images is essential for numerous applications. Recently, Neural Radiance Fields (NeRFs) have emerged as a promising framework for 3D modeling. However, NeRFs require accurate camera poses as input, and existing methods
Externí odkaz:
http://arxiv.org/abs/2407.12667
3D surface reconstruction from multi-view images is essential for scene understanding and interaction. However, complex indoor scenes pose challenges such as ambiguity due to limited observations. Recent implicit surface representations, such as Neur
Externí odkaz:
http://arxiv.org/abs/2407.12661