74 citations · 520 across the 46 of their papers we have counts for
8 papers · 1 filter
SPU-Net: Self-Supervised Point Cloud Upsampling by Coarse-to-Fine Reconstruction with Self-Projection Optimization
Xinhai Liu, Xinchen Liu, Yu-Shen Liu +1
The task of point cloud upsampling aims to acquire dense and uniform point sets from sparse and irregular point sets. Although significant progress has been made with deep learning…
PMP-Net: Point Cloud Completion by Learning Multi-step Point Moving Paths
Xin Wen, Peng Xiang, Zhizhong Han +4
The task of point cloud completion aims to predict the missing part for an incomplete 3D shape. A widely used strategy is to generate a complete point cloud from the incomplete one…
Neural-Pull: Learning Signed Distance Functions from Point Clouds by Learning to Pull Space onto Surfaces
Baorui Ma, Zhizhong Han, Yu-Shen Liu +1
Reconstructing continuous surfaces from 3D point clouds is a fundamental operation in 3D geometry processing. Several recent state-of-the-art methods address this problem using neu…
DRWR: A Differentiable Renderer without Rendering for Unsupervised 3D Structure Learning from Silhouette Images
Zhizhong Han, Chao Chen, Yu-Shen Liu +1
Differentiable renderers have been used successfully for unsupervised 3D structure learning from 2D images because they can bridge the gap between 3D and 2D. To optimize 3D shape p…
Point Cloud Completion by Skip-attention Network with Hierarchical Folding
Xin Wen, Tianyang Li, Zhizhong Han +1
Point cloud completion aims to infer the complete geometries for missing regions of 3D objects from incomplete ones. Previous methods usually predict the complete point cloud based…
Fine-Grained 3D Shape Classification with Hierarchical Part-View Attentions
Xinhai Liu, Zhizhong Han, Yu-Shen Liu +1
Fine-grained 3D shape classification is important for shape understanding and analysis, which poses a challenging research problem. However, the studies on the fine-grained 3D shap…
LRC-Net: Learning Discriminative Features on Point Clouds by Encoding Local Region Contexts
Xinhai Liu, Zhizhong Han, Fangzhou Hong +2
Learning discriminative feature directly on point clouds is still challenging in the understanding of 3D shapes. Recent methods usually partition point clouds into local region set…
SeqXY2SeqZ: Structure Learning for 3D Shapes by Sequentially Predicting 1D Occupancy Segments From 2D Coordinates
Zhizhong Han, Guanhui Qiao, Yu-Shen Liu +1
Structure learning for 3D shapes is vital for 3D computer vision. State-of-the-art methods show promising results by representing shapes using implicit functions in 3D that are lea…