no code implementations • 6 Apr 2024 • Siyu Chen, Kangcheng Liu, Chen Wang, Shenghai Yuan, Jianfei Yang, Lihua Xie
Visual Odometry (VO) is vital for the navigation of autonomous systems, providing accurate position and orientation estimates at reasonable costs.
1 code implementation • 3 Dec 2023 • Kangcheng Liu
More importantly, we innovatively propose to learn to merge the over-divided clusters based on the local low-level geometric property similarities and the learned high-level feature similarities supervised by weak labels.
no code implementations • 3 Dec 2023 • Kangcheng Liu
To the best of our knowledge, there exists no unified framework that simultaneously solves the downstream high-level understanding tasks including both segmentation and detection, especially when labels are extremely limited.
1 code implementation • 1 Dec 2023 • Kangcheng Liu, Yong-Jin Liu, Kai Tang, Ming Liu, Baoquan Chen
Deep neural network models have achieved remarkable progress in 3D scene understanding while trained in the closed-set setting and with full labels.
1 code implementation • CVPR 2023 • Aoran Xiao, Jiaxing Huang, Weihao Xuan, Ruijie Ren, Kangcheng Liu, Dayan Guan, Abdulmotaleb El Saddik, Shijian Lu, Eric Xing
In addition, we design a domain randomization technique that alternatively randomizes the geometry styles of point clouds and aggregates their embeddings, ultimately leading to a generalizable model that can improve 3DSS under various adverse weather effectively.
1 code implementation • 21 Mar 2023 • Yuzhi Zhao, Lai-Man Po, Kangcheng Liu, Xuehui Wang, Wing-Yin Yu, Pengfei Xian, Yujia Zhang, Mengyang Liu
It addresses three common issues in the scribble-based video colorization area: colorization vividness, temporal consistency, and color bleeding.
1 code implementation • CVPR 2023 • Kangcheng Liu, Xinhu Zheng, Chaoqun Wang, Kai Tang, Ming Liu, Baoquan Chen
The second is that we prevent over-discrimination between 3D segments/objects and encourage grouped foreground-to-background distinctions at the segment level with adaptive feature learning in a Siamese correspondence network, which adaptively learns feature correlations within and across point cloud views effectively.
1 code implementation • 13 Feb 2023 • Kangcheng Liu
We have summarized the existing crack detection and segmentation datasets and established the largest existing benchmark dataset on the internet for crack detection and segmentation, which is open-sourced for the research community.
1 code implementation • 28 Nov 2022 • Kangcheng Liu
First and foremost, to make the model operate in a semi-supervised manner, we proposed the confidence-level-based contrastive learning to achieve instance discrimination in an explicit manner, and make the low-confidence low-quality features align with the high-confidence counterparts.
no code implementations • 6 Sep 2022 • Xu Zheng, Yunhao Luo, Chong Fu, Kangcheng Liu, Lin Wang
To this end, we propose class-aware feature consistency distillation (CFCD) that first leverages the outputs of each student as the pseudo labels and generates class-aware feature (CF) maps for knowledge transfer between the two students.
1 code implementation • 17 Dec 2020 • Kangcheng Liu, Zhi Gao, Feng Lin, Ben M. Chen
This work presents FG-Net, a general deep learning framework for large-scale point clouds understanding without voxelizations, which achieves accurate and real-time performance with a single NVIDIA GTX 1080 GPU.
Ranked #1 on Semantic Segmentation on Semantic3D