Zobrazeno 1 - 10
of 11 471
pro vyhledávání: '"Monocular depth"'
Autor:
Guo, Xiaotong1,2 (AUTHOR) guoxiaotong0420@buaa.edu.cn, Zhao, Huijie3,4 (AUTHOR) bczhang@buaa.edu.cn, Shao, Shuwei5 (AUTHOR) swshao@buaa.edu.cn, Li, Xudong1,2 (AUTHOR) xdli@buaa.edu.cn, Zhang, Baochang3,6,7 (AUTHOR) lina_17@buaa.edu.cn, Li, Na3,4 (AUTHOR)
Publikováno v:
Future Internet. Oct2024, Vol. 16 Issue 10, p375. 14p.
Autor:
Lu, Jiahao, Huang, Tianyu, Li, Peng, Dou, Zhiyang, Lin, Cheng, Cui, Zhiming, Dong, Zhen, Yeung, Sai-Kit, Wang, Wenping, Liu, Yuan
Recent developments in monocular depth estimation methods enable high-quality depth estimation of single-view images but fail to estimate consistent video depth across different frames. Recent works address this problem by applying a video diffusion
Externí odkaz:
http://arxiv.org/abs/2412.03079
Video monocular depth estimation is essential for applications such as autonomous driving, AR/VR, and robotics. Recent transformer-based single-image monocular depth estimation models perform well on single images but struggle with depth consistency
Externí odkaz:
http://arxiv.org/abs/2412.01090
Autor:
Zeng, Ziyao, Ni, Jingcheng, Wang, Daniel, Rim, Patrick, Chung, Younjoon, Yang, Fengyu, Hong, Byung-Woo, Wong, Alex
This paper explores the potential of leveraging language priors learned by text-to-image diffusion models to address ambiguity and visual nuisance in monocular depth estimation. Particularly, traditional monocular depth estimation suffers from inhere
Externí odkaz:
http://arxiv.org/abs/2411.16750
Autor:
Wang, Jinhong, Liu, Jian, Tang, Dongqi, Wang, Weiqiang, Li, Wentong, Chen, Danny, Chen, Jintai, Wu, Jian
This paper shows that the autoregressive model is an effective and scalable monocular depth estimator. Our idea is simple: We tackle the monocular depth estimation (MDE) task with an autoregressive prediction paradigm, based on two core designs. Firs
Externí odkaz:
http://arxiv.org/abs/2411.11361
Monocular depth estimation, enabled by self-supervised learning, is a key technique for 3D perception in computer vision. However, it faces significant challenges in real-world scenarios, which encompass adverse weather variations, motion blur, as we
Externí odkaz:
http://arxiv.org/abs/2410.06982
The accurate reconstruction of per-pixel depth for an image is vital for many tasks in computer graphics, computer vision, and robotics. In this paper, we present a novel approach to generate view consistent and detailed depth maps from a number of p
Externí odkaz:
http://arxiv.org/abs/2410.03861
Autor:
Zeng, Ziyao, Wu, Yangchao, Park, Hyoungseob, Wang, Daniel, Yang, Fengyu, Soatto, Stefano, Lao, Dong, Hong, Byung-Woo, Wong, Alex
We propose a method for metric-scale monocular depth estimation. Inferring depth from a single image is an ill-posed problem due to the loss of scale from perspective projection during the image formation process. Any scale chosen is a bias, typicall
Externí odkaz:
http://arxiv.org/abs/2410.02924
Self-supervised monocular depth estimation (MDE) has gained popularity for obtaining depth predictions directly from videos. However, these methods often produce scale invariant results, unless additional training signals are provided. Addressing thi
Externí odkaz:
http://arxiv.org/abs/2411.19717
Accurate 3D mapping in endoscopy enables quantitative, holistic lesion characterization within the gastrointestinal (GI) tract, requiring reliable depth and pose estimation. However, endoscopy systems are monocular, and existing methods relying on sy
Externí odkaz:
http://arxiv.org/abs/2411.17790