25 citations · 85 across the 15 of their papers we have counts for
8 papers · 1 filter
SPCNet: Stepwise Point Cloud Completion Network
Fei Hu, Honghua Chen, Xuequan Lu +5
How will you repair a physical object with large missings? You may first recover its global yet coarse shape and stepwise increase its local details. We are motivated to imitate th…
TogetherNet: Bridging Image Restoration and Object Detection Together via Dynamic Enhancement Learning
Yongzhen Wang, Xuefeng Yan, Kaiwen Zhang +4
Adverse weather conditions such as haze, rain, and snow often impair the quality of captured images, causing detection networks trained on normal images to generalize poorly in the…
Contrastive Semantic-Guided Image Smoothing Network
Jie Wang, Yongzhen Wang, Yidan Feng +5
Image smoothing is a fundamental low-level vision task that aims to preserve salient structures of an image while removing insignificant details. Deep learning has been explored in…
3DLG-Detector: 3D Object Detection via Simultaneous Local-Global Feature Learning
Baian Chen, Liangliang Nan, Haoran Xie +3
Capturing both local and global features of irregular point clouds is essential to 3D object detection (3OD). However, mainstream 3D detectors, e.g., VoteNet and its variants, eith…
GeoSegNet: Point Cloud Semantic Segmentation via Geometric Encoder-Decoder Modeling
Chen Chen, Yisen Wang, Honghua Chen +6
Semantic segmentation of point clouds, aiming to assign each point a semantic category, is critical to 3D scene understanding.Despite of significant advances in recent years, most…
UCL-Dehaze: Towards Real-world Image Dehazing via Unsupervised Contrastive Learning
Yongzhen Wang, Xuefeng Yan, Fu Lee Wang +4
While the wisdom of training an image dehazing model on synthetic hazy data can alleviate the difficulty of collecting real-world hazy/clean image pairs, it brings the well-known d…
Semi-MoreGAN: A New Semi-supervised Generative Adversarial Network for Mixture of Rain Removal
Yiyang Shen, Yongzhen Wang, Mingqiang Wei +4
Rain is one of the most common weather which can completely degrade the image quality and interfere with the performance of many computer vision tasks, especially under heavy rain…
When A Conventional Filter Meets Deep Learning: Basis Composition Learning on Image Filters
Fu Lee Wang, Yidan Feng, Haoran Xie +2
Image filters are fast, lightweight and effective, which make these conventional wisdoms preferable as basic tools in vision tasks. In practical scenarios, users have to tweak para…