2 code implementations • Asian Conference on Computer Vision 2024 • Hongda Liu, Longguang Wang, Weijun Guan, Ye Zhang, Yulan Guo
Specifically, for style modeling, we propose a style representation learning scheme to encode the style information into a compact representation.
1 code implementation • 20 Mar 2025 • Hongda Liu, Longguang Wang, Ye Zhang, Ziru Yu, Yulan Guo
Global effective receptive field plays a crucial role for image style transfer (ST) to obtain high-quality stylized results.
no code implementations • 3 Mar 2025 • Yijie Tang, Jiazhao Zhang, Yuqing Lan, Yulan Guo, Dezun Dong, Chenyang Zhu, Kai Xu
Online zero-shot 3D instance segmentation of a progressively reconstructed scene is both a critical and challenging task for embodied applications.
3D Instance Segmentation
open vocabulary 3d instance segmentation
+2
no code implementations • 7 Feb 2025 • Runqing Jiang, Ye Zhang, Longguang Wang, Pengpeng Yu, Yulan Guo
Post-training quantization (PTQ) has emerged as a promising solution for reducing the storage and computational cost of vision transformers (ViTs).
no code implementations • 5 Jan 2025 • Minglin Chen, Longguang Wang, Sheng Ao, Ye Zhang, Kai Xu, Yulan Guo
To fully leverage 2D diffusion priors in geometry and appearance generation, we introduce a semantic-guided geometry diffusion model and a semantic-geometry guided diffusion model which are finetuned on a scene dataset.
1 code implementation • 18 Dec 2024 • Jiaqi Yang, Chu'ai Zhang, Zhengbao Wang, Xinyue Cao, Xuan Ouyang, Xiyu Zhang, Zhenxuan Zeng, Zhao Zeng, Borui Lu, Zhiyi Xia, Qian Zhang, Yulan Guo, Yanning Zhang
3D point cloud registration is a fundamental problem in computer vision, computer graphics, robotics, remote sensing, and etc.
no code implementations • 26 Nov 2024 • Yukun Wang, Longguang Wang, Zhiyuan Ma, Qibin Hu, Kai Xu, Yulan Guo
Despite the typical inversion-then-editing paradigm using text-to-image (T2I) models has demonstrated promising results, directly extending it to text-to-video (T2V) models still suffers severe artifacts such as color flickering and content distortion.
1 code implementation • 9 Oct 2024 • Wentao Chao, Fuqing Duan, Yulan Guo, Guanghui Wang
Extensive experiments demonstrate the efficacy of MaskBlur in significantly enhancing the performance of existing SR methods.
no code implementations • 25 Sep 2024 • Longguang Wang, Yulan Guo, Juncheng Li, Hongda Liu, Yang Zhao, Yingqian Wang, Zhi Jin, Shuhang Gu, Radu Timofte
This paper summarizes the 3rd NTIRE challenge on stereo image super-resolution (SR) with a focus on new solutions and results.
no code implementations • 10 Jul 2024 • Xinpu Liu, Baolin Hou, Hanyun Wang, Ke Xu, Jianwei Wan, Yulan Guo
Besides the fully supervised point cloud completion task, two additional tasks including denoising completion and zero-shot learning completion are proposed in ModelNet-MPC, to simulate real-world scenarios and verify the robustness to noise and the transfer ability across categories of current methods.
1 code implementation • 1 Jul 2024 • Hongda Liu, Longguang Wang, Ye Zhang, Kaiwen Xue, Shunbo Zhou, Yulan Guo
In addition, we develop an energy distance loss to facilitate the learning of the degradation representations by introducing a bounded constraint.
no code implementations • CVPR 2024 • Chen Min, Dawei Zhao, Liang Xiao, Jian Zhao, Xinli Xu, Zheng Zhu, Lei Jin, Jianshu Li, Yulan Guo, Junliang Xing, Liping Jing, Yiming Nie, Bin Dai
In this paper, we address this challenge by introducing a world model-based autonomous driving 4D representation learning framework, dubbed \emph{DriveWorld}, which is capable of pre-training from multi-camera driving videos in a spatio-temporal fashion.
1 code implementation • CVPR 2024 • Zhimin Yuan, Wankang Zeng, Yanfei Su, Weiquan Liu, Ming Cheng, Yulan Guo, Cheng Wang
3D synthetic-to-real unsupervised domain adaptive segmentation is crucial to annotating new domains.
no code implementations • 12 Mar 2024 • Runmin Cong, Ronghui Sheng, Hao Wu, Yulan Guo, Yunchao Wei, WangMeng Zuo, Yao Zhao, Sam Kwong
On the one hand, the low-level detail embedding module is designed to supplement high-frequency color information of depth features in a residual mask manner at the low-level stages.
no code implementations • 25 Jan 2024 • Minglin Chen, Weihao Yuan, Yukun Wang, Zhe Sheng, Yisheng He, Zilong Dong, Liefeng Bo, Yulan Guo
We propose a novel synchronized generation and reconstruction method to effectively optimize the NeRF.
1 code implementation • 3 Jan 2024 • Haopeng Li, Andong Deng, Jun Liu, Hossein Rahmani, Yulan Guo, Bernt Schiele, Mohammed Bennamoun, Qiuhong Ke
Reasoning over sports videos for question answering is an important task with numerous applications, such as player training and information retrieval.
no code implementations • CVPR 2024 • Longguang Wang, Juncheng Li, Yingqian Wang, Qingyong Hu, Yulan Guo
The difficulty of acquiring high-resolution (HR) and low-resolution (LR) image pairs in real scenarios limits the performance of existing learning-based image super-resolution (SR) methods in the real world.
no code implementations • CVPR 2024 • Kunhong Li, Longguang Wang, Ye Zhang, Kaiwen Xue, Shunbo Zhou, Yulan Guo
In this paper we exploit local structure information (LSI) to enhance stereo matching.
no code implementations • CVPR 2024 • Jingtao Sun, Yaonan Wang, Mingtao Feng, Yulan Guo, Ajmal Mian, Mike Zheng Shou
To this end we present a generic Language-to-4D modeling paradigm termed L4D-Track that tackles zero-shot 6-DoF \underline Track ing and shape reconstruction by learning pairwise implicit 3D representation and multi-level multi-modal alignment.
no code implementations • ICCV 2023 • Xiaoxiao Sheng, Zhiqiang Shen, Gang Xiao, Longguang Wang, Yulan Guo, Hehe Fan
Instead of contrasting the representations of clips or frames, in this paper, we propose a unified self-supervised framework by conducting contrastive learning at the point level.
1 code implementation • ICCV 2023 • Zhiqiang Shen, Xiaoxiao Sheng, Hehe Fan, Longguang Wang, Yulan Guo, Qiong Liu, Hao Wen, Xi Zhou
In this paper, we propose a Masked Spatio-Temporal Structure Prediction (MaST-Pre) method to capture the structure of point cloud videos without human annotations.
1 code implementation • ICCV 2023 • Minhao Li, Zheng Qin, Zhirui Gao, Renjiao Yi, Chenyang Zhu, Yulan Guo, Kai Xu
The commonly adopted detect-then-match approach to registration finds difficulties in the cross-modality cases due to the incompatible keypoint detection and inconsistent feature description.
1 code implementation • 25 Jul 2023 • Zheng Qin, Hao Yu, Changjian Wang, Yulan Guo, Yuxing Peng, Slobodan Ilic, Dewen Hu, Kai Xu
They seek correspondences over downsampled superpoints, which are then propagated to dense points.
no code implementations • 17 Jul 2023 • Baihong Lin, Zengrong Lin, Yulan Guo, Yulan Zhang, Jianxiao Zou, Shicai Fan
RGB-T semantic segmentation has been widely adopted to handle hard scenes with poor lighting conditions by fusing different modality features of RGB and thermal images.
Ranked #19 on
Thermal Image Segmentation
on MFN Dataset
1 code implementation • CVPR 2023 • Zhiqiang Shen, Xiaoxiao Sheng, Longguang Wang, Yulan Guo, Qiong Liu, Xi Zhou
Self-supervised learning can extract representations of good quality from solely unlabeled data, which is appealing for point cloud videos due to their high labelling cost.
1 code implementation • 20 Apr 2023 • Yingqian Wang, Longguang Wang, Zhengyu Liang, Jungang Yang, Radu Timofte, Yulan Guo
In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4.
1 code implementation • ICCV 2023 • Boyang Li, Yingqian Wang, Longguang Wang, Fei Zhang, Ting Liu, Zaiping Lin, Wei An, Yulan Guo
The core idea of this work is to recover the per-pixel mask of each target from the given single point label by using clustering approaches, which looks simple but is indeed challenging since targets are always insalient and accompanied with background clutters.
1 code implementation • CVPR 2023 • Haiping Wang, YuAn Liu, Zhen Dong, Yulan Guo, Yu-Shen Liu, Wenping Wang, Bisheng Yang
Previous multiview registration methods rely on exhaustive pairwise registration to construct a densely-connected pose graph and apply Iteratively Reweighted Least Square (IRLS) on the pose graph to compute the scan poses.
no code implementations • CVPR 2023 • Gengxin Liu, Qian Sun, Haibin Huang, Chongyang Ma, Yulan Guo, Li Yi, Hui Huang, Ruizhen Hu
First, although 3D dataset with fully annotated motion labels is limited, there are existing datasets and methods for object part semantic segmentation at large scale.
no code implementations • 6 Mar 2023 • Yulin He, Wei Chen, Ke Liang, Yusong Tan, Zhengfa Liang, Yulan Guo
Our proposed method, Pseudo-label Correction and Learning (PCL), is extensively evaluated on the MS COCO and PASCAL VOC benchmarks.
1 code implementation • ICCV 2023 • Zhengyu Liang, Yingqian Wang, Longguang Wang, Jungang Yang, Shilin Zhou, Yulan Guo
Exploiting spatial-angular correlation is crucial to light field (LF) image super-resolution (SR), but is highly challenging due to its non-local property caused by the disparities among LF images.
no code implementations • ICCV 2023 • Zhiheng Fu, Longguang Wang, Lian Xu, Zhiyong Wang, Hamid Laga, Yulan Guo, Farid Boussaid, Mohammed Bennamoun
In this paper, we thus propose an unsupervised viewpoint representation learning scheme for 3D point cloud completion without explicit viewpoint estimation.
1 code implementation • CVPR 2023 • Sheng Ao, Qingyong Hu, Hanyun Wang, Kai Xu, Yulan Guo
Extensive experiments on real-world scenarios demonstrate that our method achieves the best of both worlds in accuracy, efficiency, and generalization.
no code implementations • CVPR 2023 • Mingtao Feng, Haoran Hou, Liang Zhang, Zijie Wu, Yulan Guo, Ajmal Mian
In-depth understanding of a 3D scene not only involves locating/recognizing individual objects, but also requires to infer the relationships and interactions among them.
1 code implementation • CVPR 2023 • Zhao Jin, Munawar Hayat, Yuwei Yang, Yulan Guo, Yinjie Lei
The current approaches for 3D visual reasoning are task-specific, and lack pre-training methods to learn generic representations that can transfer across various tasks.
no code implementations • 23 Dec 2022 • Zi Wang, Minglin Chen, Yulan Guo, Zhang Li, Qifeng Yu
Recently, unsupervised domain adaptation in satellite pose estimation has gained increasing attention, aiming at alleviating the annotation cost for training deep models.
1 code implementation • 28 Sep 2022 • Tianhao Wu, Boyang Li, Yihang Luo, Yingqian Wang, Chao Xiao, Ting Liu, Jungang Yang, Wei An, Yulan Guo
Due to the extremely large image coverage area (e. g., thousands square kilometers), candidate targets in these images are much smaller, dimer, more changeable than those targets observed by aerial-based and land-based imaging devices.
3 code implementations • 13 Jun 2022 • Yingqian Wang, Zhengyu Liang, Longguang Wang, Jungang Yang, Wei An, Yulan Guo
In our method, a practical LF degradation model is developed to formulate the degradation process of real LF images.
no code implementations • 22 May 2022 • Changchong Sheng, Gangyao Kuang, Liang Bai, Chenping Hou, Yulan Guo, Xin Xu, Matti Pietikäinen, Li Liu
Visual speech, referring to the visual domain of speech, has attracted increasing attention due to its wide applications, such as public security, medical treatment, military defense, and film entertainment.
no code implementations • 25 Apr 2022 • Guangchi Fang, Qingyong Hu, Yiling Xu, Yulan Guo
In addition, we also propose a deep conditional entropy model to estimate the probability distribution of the transformed coefficients, by incorporating temporal context from consecutive point clouds and the motion estimation/compensation modules.
no code implementations • 20 Apr 2022 • Longguang Wang, Yulan Guo, Yingqian Wang, Juncheng Li, Shuhang Gu, Radu Timofte
In this paper, we summarize the 1st NTIRE challenge on stereo image super-resolution (restoration of rich details in a pair of low-resolution stereo images) with a focus on new solutions and results.
no code implementations • CVPR 2022 • Junhua Xi, Yifei Shi, Yijie Wang, Yulan Guo, Kai Xu
In particular, we propose RayMVSNet which learns sequential prediction of a 1D implicit field along each camera ray with the zero-crossing point indicating scene depth.
1 code implementation • CVPR 2022 • Duo Peng, Yinjie Lei, Munawar Hayat, Yulan Guo, Wen Li
In this paper, we address domain generalized semantic segmentation, where a segmentation model is trained to be domain-invariant without using any target domain data.
1 code implementation • CVPR 2022 • Yifan Zhang, Qingyong Hu, Guoquan Xu, Yanxin Ma, Jianwei Wan, Yulan Guo
To reduce the memory and computational cost, existing point-based pipelines usually adopt task-agnostic random sampling or farthest point sampling to progressively downsample input point clouds, despite the fact that not all points are equally important to the task of object detection.
1 code implementation • CVPR 2022 • Yuhua Xu, Xiaoli Yang, Yushan Yu, Wei Jia, Zhaobi Chu, Yulan Guo
In order to verify the effectiveness of the proposed system, we build a prototype and collect a test dataset in indoor scenes.
1 code implementation • CVPR 2022 • Guangchi Fang, Qingyong Hu, Hanyun Wang, Yiling Xu, Yulan Guo
Finally, the estimated probabilities are used to further compress these transform coefficients to a final attributes bitstream.
1 code implementation • CVPR 2022 • Yingqian Wang, Longguang Wang, Zhengyu Liang, Jungang Yang, Wei An, Yulan Guo
Based on the proposed cost constructor, we develop a deep network for LF depth estimation.
no code implementations • 22 Feb 2022 • Yingqian Wang, Longguang Wang, Gaochang Wu, Jungang Yang, Wei An, Jingyi Yu, Yulan Guo
In this paper, we propose a generic mechanism to disentangle these coupled information for LF image processing.
2 code implementations • CVPR 2022 • Zheng Qin, Hao Yu, Changjian Wang, Yulan Guo, Yuxing Peng, Kai Xu
Such sparse and loose matching requires contextual features capturing the geometric structure of the point clouds.
no code implementations • 9 Jan 2022 • Yan Liu, Qingyong Hu, Yinjie Lei, Kai Xu, Jonathan Li, Yulan Guo
In this paper, we introduce a neural architecture, termed Box2Seg, to learn point-level semantics of 3D point clouds with bounding box-level supervision.
1 code implementation • CVPR 2022 • Kunhong Li, Longguang Wang, Li Liu, Qing Ran, Kai Xu, Yulan Guo
Weakly supervised learning can help local feature methods to overcome the obstacle of acquiring a large-scale dataset with densely labeled correspondences.
Ranked #1 on
Camera Localization
on Aachen Day-Night benchmark
1 code implementation • CVPR 2022 • Longguang Wang, Xiaoyu Dong, Yingqian Wang, Li Liu, Wei An, Yulan Guo
Since a linear quantizer (i. e., round(*) function) cannot well fit the bell-shaped distributions of weights and activations, many existing methods use pre-defined functions (e. g., exponential function) with learnable parameters to build the quantizer for joint optimization.
1 code implementation • 25 Nov 2021 • Qian Yin, Qingyong Hu, Hao liu, Feng Zhang, Yingqian Wang, Zaiping Lin, Wei An, Yulan Guo
Satellite video cameras can provide continuous observation for a large-scale area, which is important for many remote sensing applications.
no code implementations • 19 Oct 2021 • Yimin Wei, Hao liu, TingTing Xie, Qiuhong Ke, Yulan Guo
We test the effectiveness our PST2 with two different tasks on point cloud sequences, i. e., 4D semantic segmentation and 3D action recognition.
1 code implementation • 9 Aug 2021 • Yingqian Wang, Jungang Yang, Yulan Guo, Chao Xiao, Wei An
In this letter, we propose a light field refocusing method to improve the imaging quality of camera arrays.
1 code implementation • ICCV 2021 • Duo Peng, Yinjie Lei, Wen Li, Pingping Zhang, Yulan Guo
Domain adaptation is critical for success when confronting with the lack of annotations in a new domain.
1 code implementation • 6 Jul 2021 • Qingyong Hu, Bo Yang, Linhai Xie, Stefano Rosa, Yulan Guo, Zhihua Wang, Niki Trigoni, Andrew Markham
We study the problem of efficient semantic segmentation of large-scale 3D point clouds.
1 code implementation • CVPR 2021 • Bin Xu, Yuhua Xu, Xiaoli Yang, Wei Jia, Yulan Guo
In this paper, we present a novel edge-preserving cost volume upsampling module based on the slicing operation in the learned bilateral grid.
1 code implementation • 1 Jun 2021 • Boyang Li, Chao Xiao, Longguang Wang, Yingqian Wang, Zaiping Lin, Miao Li, Wei An, Yulan Guo
With the repeated interaction in DNIM, infrared small targets in deep layers can be maintained.
2 code implementations • 11 Apr 2021 • Qingyong Hu, Bo Yang, Guangchi Fang, Yulan Guo, Ales Leonardis, Niki Trigoni, Andrew Markham
Labelling point clouds fully is highly time-consuming and costly.
2 code implementations • CVPR 2021 • Longguang Wang, Yingqian Wang, Xiaoyu Dong, Qingyu Xu, Jungang Yang, Wei An, Yulan Guo
In this paper, we propose an unsupervised degradation representation learning scheme for blind SR without explicit degradation estimation.
no code implementations • 26 Jan 2021 • Delu Zeng, Minyu Liao, Mohammad Tavakolian, Yulan Guo, Bolei Zhou, Dewen Hu, Matti Pietikäinen, Li Liu
Scene classification, aiming at classifying a scene image to one of the predefined scene categories by comprehending the entire image, is a longstanding, fundamental and challenging problem in computer vision.
1 code implementation • CVPR 2021 • Sheng Ao, Qingyong Hu, Bo Yang, Andrew Markham, Yulan Guo
Extracting robust and general 3D local features is key to downstream tasks such as point cloud registration and reconstruction.
Ranked #2 on
Point Cloud Registration
on ETH (trained on 3DMatch)
1 code implementation • 7 Nov 2020 • Yingqian Wang, Xinyi Ying, Longguang Wang, Jungang Yang, Wei An, Yulan Guo
Although recent years have witnessed the great advances in stereo image super-resolution (SR), the beneficial information provided by binocular systems has not been fully used.
no code implementations • 18 Oct 2020 • Hong-Xiang Chen, Kunhong Li, Zhiheng Fu, Mengyi Liu, Zonghao Chen, Yulan Guo
A main challenge for tasks on panorama lies in the distortion of objects among images.
1 code implementation • 11 Oct 2020 • Isaac Ronald Ward, Jack Joyner, Casey Lickfold, Yulan Guo, Mohammed Bennamoun
Graph neural networks (GNNs) have recently grown in popularity in the field of artificial intelligence (AI) due to their unique ability to ingest relatively unstructured data types as input data.
2 code implementations • 16 Sep 2020 • Longguang Wang, Yulan Guo, Yingqian Wang, Zhengfa Liang, Zaiping Lin, Jungang Yang, Wei An
Based on our PAM, we propose a parallax-attention stereo matching network (PASMnet) and a parallax-attention stereo image super-resolution network (PASSRnet) for stereo matching and stereo image super-resolution tasks.
3 code implementations • 5 Aug 2020 • Ruigang Fu, Qingyong Hu, Xiaohu Dong, Yulan Guo, Yinghui Gao, Biao Li
To have a better understanding and usage of Convolution Neural Networks (CNNs), the visualization and interpretation of CNNs has attracted increasing attention in recent years.
1 code implementation • 7 Jul 2020 • Yingqian Wang, Jungang Yang, Longguang Wang, Xinyi Ying, Tianhao Wu, Wei An, Yulan Guo
In this paper, we propose a deformable convolution network (i. e., LF-DFnet) to handle the disparity problem for LF image SR.
no code implementations • 20 Jun 2020 • Haojie Liu, Kang Liao, Chunyu Lin, Yao Zhao, Yulan Guo
Pseudo-LiDAR point cloud interpolation is a novel and challenging task in the field of autonomous driving, which aims to address the frequency mismatching problem between camera and LiDAR.
1 code implementation • CVPR 2021 • Longguang Wang, Xiaoyu Dong, Yingqian Wang, Xinyi Ying, Zaiping Lin, Wei An, Yulan Guo
Specifically, we develop a Sparse Mask SR (SMSR) network to learn sparse masks to prune redundant computation.
no code implementations • 26 May 2020 • Siyu Hong, Kunhong Li, Yongcong Zhang, Zhiheng Fu, Mengyi Liu, Yulan Guo
Most existing methods use detect-then-describe or detect-and-describe strategy to learn local features without considering their context information.
2 code implementations • ICCV 2021 • Longguang Wang, Yingqian Wang, Zaiping Lin, Jungang Yang, Wei An, Yulan Guo
In this paper, we propose to learn a scale-arbitrary image SR network from scale-specific networks.
1 code implementation • 6 Apr 2020 • Xinyi Ying, Longguang Wang, Yingqian Wang, Weidong Sheng, Wei An, Yulan Guo
In this paper, we propose a deformable 3D convolution network (D3Dnet) to incorporate spatio-temporal information from both spatial and temporal dimensions for video SR.
2 code implementations • 6 Jan 2020 • Longguang Wang, Yulan Guo, Li Liu, Zaiping Lin, Xinpu Deng, Wei An
The key challenge for video SR lies in the effective exploitation of temporal dependency between consecutive frames.
Ranked #6 on
Video Super-Resolution
on MSU Super-Resolution for Video Compression
(BSQ-rate over ERQA metric)
3 code implementations • 27 Dec 2019 • Yulan Guo, Hanyun Wang, Qingyong Hu, Hao liu, Li Liu, Mohammed Bennamoun
To stimulate future research, this paper presents a comprehensive review of recent progress in deep learning methods for point clouds.
1 code implementation • 17 Dec 2019 • Yingqian Wang, Longguang Wang, Jungang Yang, Wei An, Jingyi Yu, Yulan Guo
Specifically, spatial and angular features are first separately extracted from input LFs, and then repetitively interacted to progressively incorporate spatial and angular information.
1 code implementation • 10 Dec 2019 • Yingqian Wang, Tianhao Wu, Jungang Yang, Longguang Wang, Wei An, Yulan Guo
In this paper, we handle the LF de-occlusion (LF-DeOcc) problem using a deep encoder-decoder network (namely, DeOccNet).
9 code implementations • CVPR 2020 • Qingyong Hu, Bo Yang, Linhai Xie, Stefano Rosa, Yulan Guo, Zhihua Wang, Niki Trigoni, Andrew Markham
We study the problem of efficient semantic segmentation for large-scale 3D point clouds.
Ranked #2 on
3D Semantic Segmentation
on Toronto-3D
no code implementations • 16 Sep 2019 • Haojie Liu, Kang Liao, Chunyu Lin, Yao Zhao, Yulan Guo
In this paper, we propose a novel Pseudo-LiDAR interpolation network (PLIN) to increase the frequency of LiDAR sensors.
no code implementations • CVPR 2019 • Salman H. Khan, Yulan Guo, Munawar Hayat, Nick Barnes
Using the primitive parts for shapes as attributes, a parameterized 3D representation is modeled in the first stage.
no code implementations • 15 Mar 2019 • Yingqian Wang, Longguang Wang, Jungang Yang, Wei An, Yulan Guo
With the popularity of dual cameras in recently released smart phones, a growing number of super-resolution (SR) methods have been proposed to enhance the resolution of stereo image pairs.
1 code implementation • CVPR 2019 • Longguang Wang, Yingqian Wang, Zhengfa Liang, Zaiping Lin, Jungang Yang, Wei An, Yulan Guo
Stereo image pairs can be used to improve the performance of super-resolution (SR) since additional information is provided from a second viewpoint.
Ranked #1 on
Image Super-Resolution
on KITTI 2012 - 4x upscaling
no code implementations • 1 Mar 2019 • Yinjie Lei, Ziqin Zhou, Pingping Zhang, Yulan Guo, Zijun Ma, Lingqiao Liu
A sketch based 3D shape retrieval
2 code implementations • 23 Sep 2018 • Longguang Wang, Yulan Guo, Zaiping Lin, Xinpu Deng, Wei An
Extensive experiments demonstrate that HR optical flows provide more accurate correspondences than their LR counterparts and improve both accuracy and consistency performance.
Ranked #18 on
Video Super-Resolution
on Vid4 - 4x upscaling
2 code implementations • CVPR 2018 • Zhengfa Liang, Yiliu Feng, Yulan Guo, Hengzhu Liu, Wei Chen, Linbo Qiao, Li Zhou, Jianfeng Zhang
The second part performs matching cost calculation, matching cost aggregation and disparity calculation to estimate the initial disparity using shared features.
1 code implementation • 17 Oct 2016 • Zongliang Zhang, Jonathan Li, Yulan Guo, Yangbin Lin, Ming Cheng, Cheng Wang
However, most geometric model fitting methods are unable to fit an arbitrary geometric model (e. g. a surface with holes) to incomplete data, due to that the similarity metrics used in these methods are unable to measure the rigid partial similarity between arbitrary models.
no code implementations • 11 Apr 2013 • Yulan Guo, Ferdous Sohel, Mohammed Bennamoun, Min Lu, Jianwei Wan
The performance of the proposed LRF, RoPS descriptor and object recognition algorithm was rigorously tested on a number of popular and publicly available datasets.