31 citations · 121 across the 15 of their papers we have counts for
16 papers
Towards High-Quality Specular Highlight Removal by Leveraging Large-Scale Synthetic Data
Gang Fu, Qing Zhang, Lei Zhu +2
This paper aims to remove specular highlights from a single object-level image. Although previous methods have made some progresses, their performance remains somewhat limited, par…
NeTO:Neural Reconstruction of Transparent Objects with Self-Occlusion Aware Refraction-Tracing
Zongcheng Li, Xiaoxiao Long, Yusen Wang +4
We present a novel method, called NeTO, for capturing 3D geometry of solid transparent objects from 2D images via volume rendering. Reconstructing transparent objects is a very cha…
NeuralRoom: Geometry-Constrained Neural Implicit Surfaces for Indoor Scene Reconstruction
Yusen Wang, Zongcheng Li, Yu Jiang +4
We present a novel neural surface reconstruction method called NeuralRoom for reconstructing room-sized indoor scenes directly from a set of 2D images. Recently, implicit neural re…
Video Shadow Detection via Spatio-Temporal Interpolation Consistency Training
Xiao Lu, Yihong Cao, Sheng Liu +5
It is challenging to annotate large-scale datasets for supervised video shadow detection methods. Using a model trained on labeled images to the video frames directly may lead to h…
DGECN: A Depth-Guided Edge Convolutional Network for End-to-End 6D Pose Estimation
Tuo Cao, Fei Luo, Yanping Fu +3
Monocular 6D pose estimation is a fundamental task in computer vision. Existing works often adopt a two-stage pipeline by establishing correspondences and utilizing a RANSAC algori…
Luminance Attentive Networks for HDR Image and Panorama Reconstruction
Hanning Yu, Wentao Liu, Chengjiang Long +3
It is very challenging to reconstruct a high dynamic range (HDR) from a low dynamic range (LDR) image as an ill-posed problem. This paper proposes a luminance attentive network nam…
CANet: A Context-Aware Network for Shadow Removal
Zipei Chen, Chengjiang Long, Ling Zhang +1
In this paper, we propose a novel two-stage context-aware network named CANet for shadow removal, in which the contextual information from non-shadow regions is transferred to shad…
Dual Graph Convolutional Networks with Transformer and Curriculum Learning for Image Captioning
Xinzhi Dong, Chengjiang Long, Wenju Xu +1
Existing image captioning methods just focus on understanding the relationship between objects or instances in a single image, without exploring the contextual correlation existed…
Deep Image-based Illumination Harmonization
Zhongyun Bao, Chengjiang Long, Gang Fu +4
Integrating a foreground object into a background scene with illumination harmonization is an important but challenging task in computer vision and augmented reality community. Exi…
CRD-CGAN: Category-Consistent and Relativistic Constraints for Diverse Text-to-Image Generation
Tao Hu, Chengjiang Long, Chunxia Xiao
Generating photo-realistic images from a text description is a challenging problem in computer vision. Previous works have shown promising performance to generate synthetic images…
Detail Preserved Point Cloud Completion via Separated Feature Aggregation
Wenxiao Zhang, Qingan Yan, Chunxia Xiao
Point cloud shape completion is a challenging problem in 3D vision and robotics. Existing learning-based frameworks leverage encoder-decoder architectures to recover the complete s…
RIS-GAN: Explore Residual and Illumination with Generative Adversarial Networks for Shadow Removal
Ling Zhang, Chengjiang Long, Xiaolong Zhang +1
Residual images and illumination estimation have been proved very helpful in image enhancement. In this paper, we propose a general and novel framework RIS-GAN which explores resid…
ARGAN: Attentive Recurrent Generative Adversarial Network for Shadow Detection and Removal
Bin Ding, Chengjiang Long, Ling Zhang +1
In this paper we propose an attentive recurrent generative adversarial network (ARGAN) to detect and remove shadows in an image. The generator consists of multiple progressive step…
VITAL: A Visual Interpretation on Text with Adversarial Learning for Image Labeling
Tao Hu, Chengjiang Long, Leheng Zhang +1
In this paper, we propose a novel way to interpret text information by extracting visual feature presentation from multiple high-resolution and photo-realistic synthetic images gen…
Enhancing Underexposed Photos using Perceptually Bidirectional Similarity
Qing Zhang, Yongwei Nie, Lei Zhu +2
Although remarkable progress has been made, existing methods for enhancing underexposed photos tend to produce visually unpleasing results due to the existence of visual artifacts…
PCAN: 3D Attention Map Learning Using Contextual Information for Point Cloud Based Retrieval
Wenxiao Zhang, Chunxia Xiao
Point cloud based retrieval for place recognition is an emerging problem in vision field. The main challenge is how to find an efficient way to encode the local features into a dis…