Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Lin, Haotong"'
Autor:
Lin, Haotong, Peng, Sida, Chen, Jingxiao, Peng, Songyou, Sun, Jiaming, Liu, Minghuan, Bao, Hujun, Feng, Jiashi, Zhou, Xiaowei, Kang, Bingyi
Prompts play a critical role in unleashing the power of language and vision foundation models for specific tasks. For the first time, we introduce prompting into depth foundation models, creating a new paradigm for metric depth estimation termed Prom
Externí odkaz:
http://arxiv.org/abs/2412.14015
Autor:
Yan, Yunzhi, Xu, Zhen, Lin, Haotong, Jin, Haian, Guo, Haoyu, Wang, Yida, Zhan, Kun, Lang, Xianpeng, Bao, Hujun, Zhou, Xiaowei, Peng, Sida
This paper aims to tackle the problem of photorealistic view synthesis from vehicle sensor data. Recent advancements in neural scene representation have achieved notable success in rendering high-quality autonomous driving scenes, but the performance
Externí odkaz:
http://arxiv.org/abs/2412.13188
Autor:
Yan, Yunzhi, Lin, Haotong, Zhou, Chenxu, Wang, Weijie, Sun, Haiyang, Zhan, Kun, Lang, Xianpeng, Zhou, Xiaowei, Peng, Sida
This paper aims to tackle the problem of modeling dynamic urban streets for autonomous driving scenes. Recent methods extend NeRF by incorporating tracked vehicle poses to animate vehicles, enabling photo-realistic view synthesis of dynamic urban str
Externí odkaz:
http://arxiv.org/abs/2401.01339
Autor:
Xu, Zhen, Xie, Tao, Peng, Sida, Lin, Haotong, Shuai, Qing, Yu, Zhiyuan, He, Guangzhao, Sun, Jiaming, Bao, Hujun, Zhou, Xiaowei
Volumetric video is a technology that digitally records dynamic events such as artistic performances, sporting events, and remote conversations. When acquired, such volumography can be viewed from any viewpoint and timestamp on flat screens, 3D displ
Externí odkaz:
http://arxiv.org/abs/2312.06575
Autor:
Xu, Zhen, Peng, Sida, Lin, Haotong, He, Guangzhao, Sun, Jiaming, Shen, Yujun, Bao, Hujun, Zhou, Xiaowei
This paper targets high-fidelity and real-time view synthesis of dynamic 3D scenes at 4K resolution. Recently, some methods on dynamic view synthesis have shown impressive rendering quality. However, their speed is still limited when rendering high-r
Externí odkaz:
http://arxiv.org/abs/2310.11448
This paper aims to tackle the challenge of dynamic view synthesis from multi-view videos. The key observation is that while previous grid-based methods offer consistent rendering, they fall short in capturing appearance details of a complex dynamic s
Externí odkaz:
http://arxiv.org/abs/2310.08585
Autor:
Lin, Haotong, Wang, Qianqian, Cai, Ruojin, Peng, Sida, Averbuch-Elor, Hadar, Zhou, Xiaowei, Snavely, Noah
In this work, we aim to reconstruct a time-varying 3D model, capable of rendering photo-realistic renderings with independent control of viewpoint, illumination, and time, from Internet photos of large-scale landmarks. The core challenges are twofold
Externí odkaz:
http://arxiv.org/abs/2306.07970
A fully automated object reconstruction pipeline is crucial for digital content creation. While the area of 3D reconstruction has witnessed profound developments, the removal of background to obtain a clean object model still relies on different form
Externí odkaz:
http://arxiv.org/abs/2305.08810
Autor:
Zhang, Shangzhan, Peng, Sida, Chen, Tianrun, Mou, Linzhan, Lin, Haotong, Yu, Kaicheng, Liao, Yiyi, Zhou, Xiaowei
We introduce a novel approach that takes a single semantic mask as input to synthesize multi-view consistent color images of natural scenes, trained with a collection of single images from the Internet. Prior works on 3D-aware image synthesis either
Externí odkaz:
http://arxiv.org/abs/2302.07224
Autor:
Guo, Haoyu, Peng, Sida, Lin, Haotong, Wang, Qianqian, Zhang, Guofeng, Bao, Hujun, Zhou, Xiaowei
This paper addresses the challenge of reconstructing 3D indoor scenes from multi-view images. Many previous works have shown impressive reconstruction results on textured objects, but they still have difficulty in handling low-textured planar regions
Externí odkaz:
http://arxiv.org/abs/2205.02836