Zobrazeno 1 - 10
of 22 479
pro vyhledávání: '"dynamic scenes"'
Reconstructing dynamic scenes from video sequences is a highly promising task in the multimedia domain. While previous methods have made progress, they often struggle with slow rendering and managing temporal complexities such as significant motion a
Externí odkaz:
http://arxiv.org/abs/2412.06299
Autor:
Gao, Qiankun, Wu, Yanmin, Wen, Chengxiang, Meng, Jiarui, Tang, Luyang, Chen, Jie, Wang, Ronggang, Zhang, Jian
Reconstructing dynamic scenes with large-scale and complex motions remains a significant challenge. Recent techniques like Neural Radiance Fields and 3D Gaussian Splatting (3DGS) have shown promise but still struggle with scenes involving substantial
Externí odkaz:
http://arxiv.org/abs/2412.02493
Hyperspectral 3D imaging captures both depth maps and hyperspectral images, enabling comprehensive geometric and material analysis. Recent methods achieve high spectral and depth accuracy; however, they require long acquisition times often over sever
Externí odkaz:
http://arxiv.org/abs/2412.01140
In this paper, we propose a novel method for monocular depth estimation in dynamic scenes. We first explore the arbitrariness of object's movement trajectory in dynamic scenes theoretically. To overcome the arbitrariness, we use assume that points mo
Externí odkaz:
http://arxiv.org/abs/2411.04227
Volumetric reconstruction of dynamic scenes is an important problem in computer vision. It is especially challenging in poor lighting and with fast motion. It is partly due to the limitations of RGB cameras: To capture fast motion without much blur,
Externí odkaz:
http://arxiv.org/abs/2412.06770
Autor:
Liang, Hanxue, Ren, Jiawei, Mirzaei, Ashkan, Torralba, Antonio, Liu, Ziwei, Gilitschenski, Igor, Fidler, Sanja, Oztireli, Cengiz, Ling, Huan, Gojcic, Zan, Huang, Jiahui
Recent advancements in static feed-forward scene reconstruction have demonstrated significant progress in high-quality novel view synthesis. However, these models often struggle with generalizability across diverse environments and fail to effectivel
Externí odkaz:
http://arxiv.org/abs/2412.03526
Depth estimation is a crucial technology in robotics. Recently, self-supervised depth estimation methods have demonstrated great potential as they can efficiently leverage large amounts of unlabelled real-world data. However, most existing methods ar
Externí odkaz:
http://arxiv.org/abs/2411.04826
Recent advancements in high-fidelity dynamic scene reconstruction have leveraged dynamic 3D Gaussians and 4D Gaussian Splatting for realistic scene representation. However, to make these methods viable for real-time applications such as AR/VR, gaming
Externí odkaz:
http://arxiv.org/abs/2412.05700
LiDAR scene generation has been developing rapidly recently. However, existing methods primarily focus on generating static and single-frame scenes, overlooking the inherently dynamic nature of real-world driving environments. In this work, we introd
Externí odkaz:
http://arxiv.org/abs/2410.18084
Autor:
Zhang, Xinjie, Liu, Zhening, Zhang, Yifan, Ge, Xingtong, He, Dailan, Xu, Tongda, Wang, Yan, Lin, Zehong, Yan, Shuicheng, Zhang, Jun
4D Gaussian Splatting (4DGS) has recently emerged as a promising technique for capturing complex dynamic 3D scenes with high fidelity. It utilizes a 4D Gaussian representation and a GPU-friendly rasterizer, enabling rapid rendering speeds. Despite it
Externí odkaz:
http://arxiv.org/abs/2410.13613