no code implementations • 17 May 2024 • Kyle Gao, Dening Lu, Hongjie He, Linlin Xu, Jonathan Li
3D urban scene reconstruction and modelling is a crucial research area in remote sensing with numerous applications in academia, commerce, industry, and administration.
no code implementations • 16 May 2024 • Muhammed Patel, Xinwei Chen, Linlin Xu, Yuhao Chen, K Andrea Scott, David A. Clausi
Fully supervised deep learning approaches have demonstrated impressive accuracy in sea ice classification, but their dependence on high-resolution labels presents a significant challenge due to the difficulty of obtaining such data.
no code implementations • 12 May 2024 • Aaryam Sharma, Chris Czarnecki, Yuhao Chen, Pengcheng Xi, Linlin Xu, Alexander Wong
Monitoring dietary intake is a crucial aspect of promoting healthy living.
no code implementations • 12 May 2024 • Akil Pathiranage, Chris Czarnecki, Yuhao Chen, Pengcheng Xi, Linlin Xu, Alexander Wong
Ellipse estimation is an important topic in food image processing because it can be leveraged to parameterize plates and bowls, which in turn can be used to estimate camera view angles and food portion sizes.
no code implementations • 2 May 2024 • Zhengsen Xu, Jonathan Li, Linlin Xu
In this technical review, we describe the options for independent variables, data processing techniques, models, independent variables collinearity and importance estimation methods, and model performance evaluation metrics.
1 code implementation • ICCV 2023 • Jun Zhou, Kai Chen, Linlin Xu, Qi Dou, Jing Qin
One critical challenge in 6D object pose estimation from a single RGBD image is efficient integration of two different modalities, i. e., color and depth.
no code implementations • 30 May 2023 • Dening Lu, Jun Zhou, Kyle Yilin Gao, Dilong Li, Jing Du, Linlin Xu, Jonathan Li
Specifically, we propose novel semantic feature-based dynamic sampling and clustering methods in the encoder, which enables the model to be aware of local semantic homogeneity for local feature aggregation.
no code implementations • 1 Oct 2022 • Kyle Gao, Yina Gao, Hongjie He, Dening Lu, Linlin Xu, Jonathan Li
Neural Radiance Field (NeRF) has recently become a significant development in the field of Computer Vision, allowing for implicit, neural network-based scene representation and novel view synthesis.
no code implementations • 21 Sep 2022 • Dening Lu, Kyle Gao, Qian Xie, Linlin Xu, Jonathan Li
This paper presents a novel point cloud representational learning network, called 3D Dual Self-attention Global Local (GLocal) Transformer Network (3DGTN), for improved feature learning in both classification and segmentation tasks, with the following key contributions.
no code implementations • 16 May 2022 • Dening Lu, Qian Xie, Mingqiang Wei, Kyle Gao, Linlin Xu, Jonathan Li
To demonstrate the superiority of Transformers in point cloud analysis, we present comprehensive comparisons of various Transformer-based methods for classification, segmentation, and object detection.
1 code implementation • 2 Mar 2022 • Dening Lu, Qian Xie, Linlin Xu, Jonathan Li
This paper presents a novel hierarchical framework that incorporates convolution with Transformer for point cloud classification, named 3D Convolution-Transformer Network (3DCTN), to combine the strong and efficient local feature learning ability of convolution with the remarkable global context modeling capability of Transformer.
no code implementations • 12 Jul 2021 • Peter Q. Lee, Linlin Xu, David A. Clausi
We consider a linear denoising model that re-scales the noise field for each subswath, whose parameters are found from a least-squares solution over the objective function.
no code implementations • 16 Mar 2021 • Hongjie He, Ke Yang, Yuwei Cai, Zijian Jiang, Qiutong Yu, Kun Zhao, JunBo Wang, Sarah Narges Fatholahi, Yan Liu, Hasti Andon Petrosians, Bingxu Hu, Liyuan Qing, Zhehan Zhang, Hongzhang Xu, Siyu Li, Kyle Gao, Linlin Xu, Jonathan Li
Building rooftop data are of importance in several urban applications and in natural disaster management.
no code implementations • 7 Oct 2020 • Yun Cao, Yuebin Wang, Junhuan Peng, Liqiang Zhang, Linlin Xu, Kai Yan, Lihua Li
With a small number of labeled samples for training, it can save considerable manpower and material resources, especially when the amount of high spatial resolution remote sensing images (HSR-RSIs) increases considerably.
no code implementations • 10 Jul 2020 • Yuhao Chen, Yifan Wu, Linlin Xu, Alexander Wong
In this paper, we leverage the performance of CNNs, and propose a module that uses prior knowledge of building corners to create angular and concise building polygons from CNN segmentation outputs.