activity
20172022
most citedLarge-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55

53 citations · 149 across the 10 of their papers we have counts for

collaborators

24 papers

cs.CV20229 cited

Towards Self-Supervised Category-Level Object Pose and Size Estimation

Yisheng He, Haoqiang Fan, Haibin Huang +2

In this work, we tackle the challenging problem of category-level object pose and size estimation from a single depth image. Although previous fully-supervised works have demonstra…

eess.IV20228 cited

Deep Constrained Least Squares for Blind Image Super-Resolution

Ziwei Luo, Haibin Huang, Lei Yu +3

In this paper, we tackle the problem of blind image super-resolution(SR) with a reformulated degradation model and two novel modules. Following the common practices of blind SR, ou…

cs.CV20213 cited

iShape: A First Step Towards Irregular Shape Instance Segmentation

Lei Yang, Yan Zi Wei, Yisheng HE +4

In this paper, we introduce a brand new dataset to promote the study of instance segmentation for objects with irregular shapes. Our key observation is that though irregularly shap…

cs.CV20211 cited

Scene Synthesis via Uncertainty-Driven Attribute Synchronization

Haitao Yang, Zaiwei Zhang, Siming Yan +5

Developing deep neural networks to generate 3D scenes is a fundamental problem in neural synthesis with immediate applications in architectural CAD, computer graphics, as well as i…

cs.CV2021

HPNet: Deep Primitive Segmentation Using Hybrid Representations

Siming Yan, Zhenpei Yang, Chongyang Ma +3

This paper introduces HPNet, a novel deep-learning approach for segmenting a 3D shape represented as a point cloud into primitive patches. The key to deep primitive segmentation is…

cs.CV202114 cited

FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation

Yisheng He, Haibin Huang, Haoqiang Fan +2

In this work, we present FFB6D, a Full Flow Bidirectional fusion network designed for 6D pose estimation from a single RGBD image. Our key insight is that appearance information in…