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 …

Recent advancements in 3D reconstruction from single images have been driven by the
evolution of generative models. Prominent among these are methods based on Score …

In recent years, deep learning techniques for processing 3D point cloud data have seen
significant advancements, given their unique ability to extract relevant features and handle …

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 …

Transformer, an attention-based encoder–decoder model, has already revolutionized the
field of natural language processing (NLP). Inspired by such significant achievements, some …

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 …

Point cloud completion aims to recover the completed 3D shape of an object from its partial
observation. A common strategy is to encode the observed points to a global feature vector …

We present ShapeFormer, a transformer-based network that produces a distribution of
object completions, conditioned on incomplete, and possibly noisy, point clouds. The …

Surface reconstruction for point clouds is an important task in 3D computer vision. Most of
the latest methods resolve this problem by learning signed distance functions (SDF) from …