Zobrazeno 1 - 10
of 986
pro vyhledávání: '"ZHANG, TIANZHU"'
Semantic segmentation often suffers from significant performance degradation when the trained network is applied to a different domain. To address this issue, unsupervised domain adaptation (UDA) has been extensively studied. Existing methods introdu
Externí odkaz:
http://arxiv.org/abs/2412.10339
Scene reconstruction from casually captured videos has wide applications in real-world scenarios. With recent advancements in differentiable rendering techniques, several methods have attempted to simultaneously optimize scene representations (NeRF o
Externí odkaz:
http://arxiv.org/abs/2410.15392
Autor:
Lu, Jiahao, Deng, Jiacheng, Zhu, Ruijie, Liang, Yanzhe, Yang, Wenfei, Zhang, Tianzhu, Zhou, Xu
Dynamic scenes rendering is an intriguing yet challenging problem. Although current methods based on NeRF have achieved satisfactory performance, they still can not reach real-time levels. Recently, 3D Gaussian Splatting (3DGS) has garnered researche
Externí odkaz:
http://arxiv.org/abs/2410.13607
Autor:
Zhu, Ruijie, Liang, Yanzhe, Chang, Hanzhi, Deng, Jiacheng, Lu, Jiahao, Yang, Wenfei, Zhang, Tianzhu, Zhang, Yongdong
Dynamic scene reconstruction is a long-term challenge in the field of 3D vision. Recently, the emergence of 3D Gaussian Splatting has provided new insights into this problem. Although subsequent efforts rapidly extend static 3D Gaussian to dynamic sc
Externí odkaz:
http://arxiv.org/abs/2410.07707
Panoramic images provide comprehensive scene information and are suitable for VR applications. Obtaining corresponding depth maps is essential for achieving immersive and interactive experiences. However, panoramic depth estimation presents significa
Externí odkaz:
http://arxiv.org/abs/2410.05735
Monocular depth estimation aims to infer a dense depth map from a single image, which is a fundamental and prevalent task in computer vision. Many previous works have shown impressive depth estimation results through carefully designed network struct
Externí odkaz:
http://arxiv.org/abs/2409.02494
Semantic segmentation of night-time images holds significant importance in computer vision, particularly for applications like night environment perception in autonomous driving systems. However, existing methods tend to parse night-time images from
Externí odkaz:
http://arxiv.org/abs/2408.13838
Point cloud few-shot semantic segmentation (PC-FSS) aims to segment targets of novel categories in a given query point cloud with only a few annotated support samples. The current top-performing prototypical learning methods employ prototypes origina
Externí odkaz:
http://arxiv.org/abs/2408.13752
3D object detection is essential for understanding 3D scenes. Contemporary techniques often require extensive annotated training data, yet obtaining point-wise annotations for point clouds is time-consuming and laborious. Recent developments in semi-
Externí odkaz:
http://arxiv.org/abs/2408.00286
Estimating depth from a single image is a challenging visual task. Compared to relative depth estimation, metric depth estimation attracts more attention due to its practical physical significance and critical applications in real-life scenarios. How
Externí odkaz:
http://arxiv.org/abs/2407.08187