activity
20192023
most citedUnmixing Convolutional Features for Crisp Edge Detection

4 citations · 5 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2023★ 1 cited

Learning Deformable Hypothesis Sampling for Accurate PatchMatch Multi-View Stereo

Hongjie Li, Yao Guo, Xianwei Zheng +1

This paper introduces a learnable Deformable Hypothesis Sampler (DeformSampler) to address the challenging issue of noisy depth estimation for accurate PatchMatch Multi-View Stereo…

cs.CV2023

Shape Anchor Guided Holistic Indoor Scene Understanding

Mingyue Dong, Linxi Huan, Hanjiang Xiong +2

This paper proposes a shape anchor guided learning strategy (AncLearn) for robust holistic indoor scene understanding. We observe that the search space constructed by current metho…

cs.CV2023

Holistic Geometric Feature Learning for Structured Reconstruction

Ziqiong Lu, Linxi Huan, Qiyuan Ma +1

The inference of topological principles is a key problem in structured reconstruction. We observe that wrongly predicted topological relationships are often incurred by the lack of…

cs.CV2022

HoW-3D: Holistic 3D Wireframe Perception from a Single Image

Wenchao Ma, Bin Tan, Nan Xue +3

This paper studies the problem of holistic 3D wireframe perception (HoW-3D), a new task of perceiving both the visible 3D wireframes and the invisible ones from single-view 2D imag…

cs.CV2020★ 4 cited

Unmixing Convolutional Features for Crisp Edge Detection

Linxi Huan, Nan Xue, Xianwei Zheng +3

This paper presents a context-aware tracing strategy (CATS) for crisp edge detection with deep edge detectors, based on an observation that the localization ambiguity of deep edge…

cs.CV2019

ELKPPNet: An Edge-aware Neural Network with Large Kernel Pyramid Pooling for Learning Discriminative Features in Semantic Segmentation

Xianwei Zheng, Linxi Huan, Hanjiang Xiong +1

Semantic segmentation has been a hot topic across diverse research fields. Along with the success of deep convolutional neural networks, semantic segmentation has made great achiev…