1 code implementation • 9 Jan 2024 • Hualie Jiang, Rui Xu, Minglang Tan, Wenjie Jiang
To bridge the gap between OSM and RAFT, we mainly propose an opposite adaptive weighting scheme to seamlessly transform the outputs of spherical sweeping of OSM into the required inputs for the recurrent update, thus creating a recurrent omnidirectional stereo matching (RomniStereo) algorithm.
1 code implementation • 23 Oct 2022 • Hualie Jiang, Rui Xu, Wenjie Jiang
Stereo-matching is a fundamental problem in computer vision.
no code implementations • 26 Aug 2022 • Junjie Hu, Chenyou Fan, Mete Ozay, Hualie Jiang, Tin Lun Lam
We study data-free knowledge distillation (KD) for monocular depth estimation (MDE), which learns a lightweight model for real-world depth perception tasks by compressing it from a trained teacher model while lacking training data in the target domain.
1 code implementation • 12 Oct 2021 • Hualie Jiang, Laiyan Ding, Junjie Hu, Rui Huang
Unsupervised learning of depth from indoor monocular videos is challenging as the artificial environment contains many textureless regions.
1 code implementation • 30 Aug 2021 • Hualie Jiang, Laiyan Ding, Zhenglong Sun, Rui Huang
We first propose an outlier masking technique that considers the occluded or dynamic pixels as statistical outliers in the photometric error map.
2 code implementations • 13 May 2021 • Junjie Hu, Chenyou Fan, Hualie Jiang, Xiyue Guo, Yuan Gao, Xiangyong Lu, Tin Lun Lam
However, this KD process can be challenging and insufficient due to the large model capacity gap between the teacher and the student.
1 code implementation • 6 Feb 2021 • Hualie Jiang, Zhe Sheng, Siyu Zhu, Zilong Dong, Rui Huang
Besides, we also designed a more effective fusion module for our fusion scheme.
Ranked #1 on Depth Estimation on Matterport3D
1 code implementation • 3 Mar 2020 • Hualie Jiang, Laiyan Ding, Zhenglong Sun, Rui Huang
Unsupervised learning of depth and ego-motion from unlabelled monocular videos has recently drawn great attention, which avoids the use of expensive ground truth in the supervised one.
Ranked #53 on Monocular Depth Estimation on KITTI Eigen split