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Density-guided Translator Boosts Synthetic-to-Real Unsupervised Domain Adaptive Segmentation of 3D Point Clouds
Zhimin Yuan;Wankang Zeng;Yanfei Su;Weiquan Liu;Ming Cheng;Yulan Guo;Cheng Wang
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
3D synthetic-to-real unsupervised domain adaptive seg-mentation is crucial to annotating new domains. Self-training is a competitive approach for this task, but its performance is limited by different sensor sampling patterns (i.e., variations in point density) and incomplete training strate-gies. In this work, we propose a density-guided translator (DGT), which translates point density between do...Show More
Multistage Scene-Level Constraints for Large-Scale Point Cloud Weakly Supervised Semantic Segmentation
Yanfei Su;Ming Cheng;Zhimin Yuan;Weiquan Liu;Wankang Zeng;Cheng Wang
Compared to fully supervised 3-D large-scale point cloud segmentation methods, which necessitate extensive manual point-wise annotations, weakly supervised segmentation has emerged as a popular approach for significantly reducing labeling costs while maintaining effectiveness. However, the existing methods have exhibited inferior segmentation performance and unsatisfactory generalization capabilit...Show More
Spatial Adaptive Fusion Consistency Contrastive Constraint: Weakly Supervised Building Facade Point Cloud Semantic Segmentation
Yanfei Su;Ming Cheng;Zhimin Yuan;Weiquan Liu;Wankang Zeng;Zhihong Zhang;Cheng Wang
Semantic segmentation of building facade point clouds has diverse applications. The development of semantic segmentation methods is inextricably linked to datasets. The available building facade datasets suffer from a lack of abundant semantic categories and data completeness. To compensate for these shortcomings, we propose a new building facade dataset characterized by various categories and rel...Show More
Novel Convolutions for Semantic Segmentation of Remote Sensing Images
The networks are required to be capable of learning low-level features well when applied to remote sensing image (RSI) semantic segmentation tasks. To capture accurate and abundant low-level semantic information, the early feature extractor layer is crucial to the whole network because all the subsequent features are inferred from that base. To address the low-level feature extraction issue and ov...Show More
Prototype-Guided Multitask Adversarial Network for Cross-Domain LiDAR Point Clouds Semantic Segmentation
Zhimin Yuan;Ming Cheng;Wankang Zeng;Yanfei Su;Weiquan Liu;Shangshu Yu;Cheng Wang
Unsupervised domain adaptation (UDA) segmentation aims to leverage labeled source data to make accurate predictions on unlabeled target data. The key is to make the segmentation network learn domain-invariant representations. In this work, we propose a prototype-guided multitask adversarial network (PMAN) to achieve this. First, we propose an intensity-aware segmentation network (IAS-Net) that lev...Show More
Contrastive Translation Learning For Medical Image Segmentation
Wankang Zeng;Wenkang Fan;Dongfang Shen;Yinran Chen;Xiongbiao Luo
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
Unsupervised domain adaptation commonly uses cycle generative networks to produce synthesis data from source to target domains. Unfortunately, translated samples cannot effectively preserve semantic information from input sources, resulting in bad or low adaptability of the network to segment target data. This work proposes an advantageous domain translation mechanism to improve the perceptual abi...Show More
Automatic Recognition of Ocular Surface Diseases on Smartphone Images Using Densely Connected Convolutional Networks
Rong Chen;Wankang Zeng;Wenkang Fan;Fang Lai;Yinran Chen;Xiang Lin;Liying Tang;Weijie Ouyang;Zuguo Liu;Xiongbiao Luo
2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Year: 2021 | Conference Paper |
Cited by: Papers (7)
Ocular surface disorder is one of common and prevalence eye diseases and complex to be recognized accurately. This work presents automatic classification of ocular surface disorders in accordance with densely connected convolutional networks and smartphone imaging. We use various smartphone cameras to collect clinical images that contain normal and abnormal, and modify end-to-end densely connected...Show More
Accurate 3d Kidney Segmentation Using Unsupervised Domain Translation And Adversarial Networks
Wankang Zeng;Wenkang Fan;Rong Chen;Zhuohui Zheng;Song Zheng;Jianhui Chen;Rong Liu;Qiang Zeng;Zengqin Liu;Yinran Chen;Xiongbiao Luo
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
Computed tomography urography imaging is routinely performed to evaluate the kidneys. Kidney 3D segmentation and reconstruction from urographic images provides physicians with an intuitive visualization way to accurately diagnose and treat kidney diseases, particularly used in surgical planning and outcome analysis before and after kidney surgery. While 3D fully convolution networks have achieved ...Show More
Robotically Surgical Vessel Localization Using Robust Hybrid Video Motion Magnification
Wenkang Fan;Zhuohui Zheng;Wankang Zeng;Yinran Chen;Hui-Qing Zeng;Hong Shi;Xiongbiao Luo
Vessel and neurovascular bundle localization plays an essential role in endoscopic and robotic surgery. It still remains challenging to spare vessels and neurovascular bundles to avoid inadvertent injury due to limited visual and tactile perception of surgeons. This work assumes that surgeons have great difficulty in intuitively perceiving small pulsatile motion of vessels and neurovascular bundle...Show More
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