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 data have been widely explored due to its superior accuracy and robustness
under various adverse situations. Meanwhile, deep neural networks (DNNs) have achieved …

Single image 3D reconstruction is an important but challenging task that requires extensive
knowledge of our natural world. Many existing methods solve this problem by optimizing a …

Point clouds captured in real-world applications are often incomplete due to the limited
sensor resolution, single viewpoint, and occlusion. Therefore, recovering the complete point …

We propose a novel approach for probabilistic generative modeling of 3D shapes. Unlike
most existing models that learn to deterministically translate a latent vector to a shape, our …

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 …

Point cloud completion has become increasingly popular among generation tasks of 3D
point clouds, as it is a challenging yet indispensable problem to recover the complete shape …

Problems such as equipment defects or limited viewpoints will lead the captured point
clouds to be incomplete. Therefore, recovering the complete point clouds from the partial …

Real-scanned point clouds are often incomplete due to viewpoint, occlusion, and noise.
Existing point cloud completion methods tend to generate global shape skeletons and …

Estimating the complete 3D point cloud from an incomplete one is a key problem in many
vision and robotics applications. Mainstream methods (eg, PCN and TopNet) use Multi-layer …