13 citations · 24 across the 6 of their papers we have counts for
9 papers · 1 filter
Learning Deep Implicit Functions for 3D Shapes with Dynamic Code Clouds
Tianyang Li, Xin Wen, Yu-Shen Liu +2
Deep Implicit Function (DIF) has gained popularity as an efficient 3D shape representation. To capture geometry details, current methods usually learn DIF using local latent codes,…
3D Shape Reconstruction from 2D Images with Disentangled Attribute Flow
Xin Wen, Junsheng Zhou, Yu-Shen Liu +2
Reconstructing 3D shape from a single 2D image is a challenging task, which needs to estimate the detailed 3D structures based on the semantic attributes from 2D image. So far, mos…
PMP-Net++: Point Cloud Completion by Transformer-Enhanced Multi-step Point Moving Paths
Xin Wen, Peng Xiang, Zhizhong Han +4
Point cloud completion concerns to predict missing part for incomplete 3D shapes. A common strategy is to generate complete shape according to incomplete input. However, unordered…
SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer
Peng Xiang, Xin Wen, Yu-Shen Liu +4
Point cloud completion aims to predict a complete shape in high accuracy from its partial observation. However, previous methods usually suffered from discrete nature of point clou…
Cycle4Completion: Unpaired Point Cloud Completion using Cycle Transformation with Missing Region Coding
Xin Wen, Zhizhong Han, Yan-Pei Cao +3
In this paper, we present a novel unpaired point cloud completion network, named Cycle4Completion, to infer the complete geometries from a partial 3D object. Previous unpaired comp…
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…