Point cloud completion is a generation and estimation issue derived from the partial point
clouds, which plays a vital role in the applications of 3D computer vision. The progress of …

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 cloud …

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 …

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 …

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 …

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 …

We propose Differentiable Surface Splatting (DSS), a high-fidelity differentiable renderer for
point clouds. Gradients for point locations and normals are carefully designed to handle …

In this paper, we proposed a novel Style-based Point Generator with Adversarial Rendering
(SpareNet) for point cloud completion. Firstly, we present the channel-attentive EdgeConv to …

Most existing point cloud completion methods suffer from the discrete nature of point clouds
and the unstructured prediction of points in local regions, which makes it difficult to reveal …

In this paper, we propose a novel network, SVDFormer, to tackle two specific challenges in
point cloud completion: understanding faithful global shapes from incomplete point clouds …