no code implementations • 15 Apr 2024 • Jiyuan Wang, Chunyu Lin, Lang Nie, Kang Liao, Shuwei Shao, Yao Zhao
In this paper, we propose a novel robust depth estimation method called D4RD, featuring a custom contrastive learning mode tailored for diffusion models to mitigate performance degradation in complex environments.
no code implementations • 27 Mar 2024 • Xiaotong Guo, Huijie Zhao, Shuwei Shao, Xudong Li, Baochang Zhang
To evaluate the generalization ability of our $\mathrm{F^2Depth}$, we collect a Campus Indoor depth dataset composed of approximately 1500 points selected from 99 images in 18 scenes.
Indoor Monocular Depth Estimation Monocular Depth Estimation +2
1 code implementation • 13 Nov 2023 • Shuwei Shao, Zhongcai Pei, Weihai Chen, Dingchi Sun, Peter C. Y. Chen, Zhengguo Li
Because the depth ground-truth is unavailable in the training phase, we develop a pseudo ground-truth diffusion process to assist the diffusion in MonoDiffusion.
1 code implementation • 13 Nov 2023 • Shuwei Shao, Zhongcai Pei, Weihai Chen, Peter C. Y. Chen, Zhengguo Li
To this end, we develop a normal-distance head that outputs pixel-level surface normal and distance.
1 code implementation • NeurIPS 2023 • Shuwei Shao, Zhongcai Pei, Xingming Wu, Zhong Liu, Weihai Chen, Zhengguo Li
To alleviate the possible error accumulation during the iterative process, we utilize a novel elastic target bin to replace the original target bin, the width of which is adjusted elastically based on the depth uncertainty.
Ranked #13 on Monocular Depth Estimation on KITTI Eigen split
1 code implementation • ICCV 2023 • Shuwei Shao, Zhongcai Pei, Weihai Chen, Xingming Wu, Zhengguo Li
Meanwhile, the normal and distance are regularized by a developed plane-aware consistency constraint.
Ranked #13 on Monocular Depth Estimation on KITTI Eigen split
1 code implementation • 20 Apr 2023 • Yongming Yang, Shuwei Shao, Tao Yang, Peng Wang, Zhuo Yang, Chengdong Wu, Hao liu
To address this issue, we introduce a gradient loss to penalize edge fluctuations ambiguous around stepped edge structures and a normal loss to explicitly express the sensitivity to frequently small structures, and propose a geometric consistency loss to spreads the spatial information across the sample grids to constrain the global geometric anatomy structures.
1 code implementation • 20 Feb 2023 • Zhong Liu, Ran Li, Shuwei Shao, Xingming Wu, Weihai Chen
In this work, we propose two novel ideas to improve self-supervised monocular depth estimation: 1) self-reference distillation and 2) disparity offset refinement.
1 code implementation • 16 Feb 2023 • Shuwei Shao, Zhongcai Pei, Weihai Chen, Ran Li, Zhong Liu, Zhengguo Li
Specifically, we use the depth estimates from the Transformer branch and the CNN branch as pseudo labels to teach each other.
Ranked #13 on Monocular Depth Estimation on KITTI Eigen split
no code implementations • 30 May 2022 • Shuwei Shao, Zhongcai Pei, Weihai Chen, Xingming Wu, Zhong Liu, Zhengguo Li
Unsupervised monocular trained depth estimation models make use of adjacent frames as a supervisory signal during the training phase.
1 code implementation • 15 Dec 2021 • Shuwei Shao, Zhongcai Pei, Weihai Chen, Wentao Zhu, Xingming Wu, Dianmin Sun, Baochang Zhang
Recently, self-supervised learning technology has been applied to calculate depth and ego-motion from monocular videos, achieving remarkable performance in autonomous driving scenarios.
no code implementations • 16 Nov 2021 • Shuwei Shao, Ran Li, Zhongcai Pei, Zhong Liu, Weihai Chen, Wentao Zhu, Xingming Wu, Baochang Zhang
In this work, we investigate into the phenomenon and propose to integrate the strengths of multiple weak depth predictor to build a comprehensive and accurate depth predictor, which is critical for many real-world applications, e. g., 3D reconstruction.