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
no code implementations • 13 Aug 2023 • Chen Wang, Zhongcai Pei, Shuang Qiu, Yachun Wang, Zhiyong Tang
Experiments on our dataset show that our method has a significant improvement over the previous best monocular vision method, with an intersection over union (IOU) increase of 3. 4 %, and the lightweight version has a fast detection speed and can meet the requirements of most real-time applications.
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 • 2 Dec 2022 • Chen Wang, Zhongcai Pei, Shuang Qiu, Zhiyong Tang
Specifically, we design a selective module, which can make the network learn the complementary relationship between the RGB map and the depth map and effectively combine the information from the RGB map and the depth map in different scenes.
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.
no code implementations • 14 Jan 2022 • Chen Wang, Zhongcai Pei, Shuang Qiu, Zhiyong Tang
Staircases are some of the most common building structures in urban environments.
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.