1 code implementation • 30 Nov 2023 • Guangming Zhu, Siyuan Wang, Qing Cheng, Kelong Wu, Hao Li, Liang Zhang
With the recent surge in the use of touchscreen devices, free-hand sketching has emerged as a promising modality for human-computer interaction.
no code implementations • 7 Oct 2023 • Wei zhang, Tiecheng Sun, Sen Wang, Qing Cheng, Norbert Haala
For global consistency, we propose an efficient Sim(3)-based pose graph bundle adjustment (PGBA) approach to run online loop closing and mitigate the pose and scale drift.
no code implementations • 2 Mar 2022 • Qing Cheng, Niclas Zeller, Daniel Cremers
In this paper, we present a complete pipeline for 3D semantic mapping solely based on a stereo camera system.
no code implementations • 7 Jul 2021 • Sihai Guan, Qing Cheng, Yong Zhao
In this paper, a family of novel diffusion adaptive estimation algorithm is proposed from the asymmetric cost function perspective by combining diffusion strategy and the linear-linear cost (LLC), quadratic-quadratic cost (QQC), and linear-exponential cost (LEC), at all distributed network nodes, and named diffusion LLCLMS (DLLCLMS), diffusion QQCLMS (DQQCLMS), and diffusion LECLMS (DLECLMS), respectively.
no code implementations • 14 Sep 2020 • Patrick Wenzel, Rui Wang, Nan Yang, Qing Cheng, Qadeer Khan, Lukas von Stumberg, Niclas Zeller, Daniel Cremers
We present a novel dataset covering seasonal and challenging perceptual conditions for autonomous driving.
no code implementations • 13 Oct 2018 • Zhiwei Li, Huanfeng Shen, Qing Cheng, Yuhao Liu, Shucheng You, Zongyi He
In this paper, we propose a deep learning based cloud detection method named multi-scale convolutional feature fusion (MSCFF) for remote sensing images of different sensors.
no code implementations • 25 Jul 2017 • Chengyue Zhang, Zhiwei Li, Qing Cheng, Xinghua Li, Huanfeng Shen
Remote sensing images often suffer from cloud cover.
no code implementations • 22 Nov 2016 • Qing Cheng, Huiqing Liu, Huanfeng Shen, Penghai Wu, Liangpei Zhang
The spatiotemporal data fusion technique is considered as a cost-effective way to obtain remote sensing data with both high spatial resolution and high temporal frequency, by blending observations from multiple sensors with different advantages or characteristics.