activity
20202022
most citedMVSTER: Epipolar Transformer for Efficient Multi-View Stereo

10 citations · 11 across the 2 of their papers we have counts for

collaborators

7 papers

cs.CV2022★ 10 cited

MVSTER: Epipolar Transformer for Efficient Multi-View Stereo

Xiaofeng Wang, Zheng Zhu, Fangbo Qin +5

Learning-based Multi-View Stereo (MVS) methods warp source images into the reference camera frustum to form 3D volumes, which are fused as a cost volume to be regularized by subseq…

cs.CV2021

ELSD: Efficient Line Segment Detector and Descriptor

Haotian Zhang, Yicheng Luo, Fangbo Qin +2

We present the novel Efficient Line Segment Detector and Descriptor (ELSD) to simultaneously detect line segments and extract their descriptors in an image. Unlike the traditional…

cs.CV2020

Multi-frame Feature Aggregation for Real-time Instrument Segmentation in Endoscopic Video

Shan Lin, Fangbo Qin, Haonan Peng +3

Deep learning-based methods have achieved promising results on surgical instrument segmentation. However, the high computation cost may limit the application of deep models to time…

cs.CV2020

Contour Primitive of Interest Extraction Network Based on One-Shot Learning for Object-Agnostic Vision Measurement

Fangbo Qin, Jie Qin, Siyu Huang +1

Image contour based vision measurement is widely applied in robot manipulation and industrial automation. It is appealing to realize object-agnostic vision system, which can be con…

cs.CV2020★ 1 cited

TP-LSD: Tri-Points Based Line Segment Detector

Siyu Huang, Fangbo Qin, Pengfei Xiong +3

This paper proposes a novel deep convolutional model, Tri-Points Based Line Segment Detector (TP-LSD), to detect line segments in an image at real-time speed. The previous related…

eess.IV2020

LC-GAN: Image-to-image Translation Based on Generative Adversarial Network for Endoscopic Images

Shan Lin, Fangbo Qin, Yangming Li +3

Intelligent vision is appealing in computer-assisted and robotic surgeries. Vision-based analysis with deep learning usually requires large labeled datasets, but manual data labeli…