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 …

Humans can easily imagine the complete 3D geometry of occluded objects and scenes. This
appealing ability is vital for recognition and understanding. To enable such capability in AI …

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 …

Learning-based perception and prediction modules in modern autonomous driving systems
typically rely on expensive human annotation and are designed to perceive only a handful of …

Multi-beam LiDAR sensors, as used on autonomous vehicles and mobile robots, acquire
sequences of 3D range scans (“frames”). Each frame covers the scene sparsely, due 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 …

We propose a self-supervised capsule architecture for 3D point clouds. We compute capsule
decompositions of objects through permutation-equivariant attention, and self-supervise the …

Deep learning technique has yielded significant improvements in point cloud completion
with the aim of completing missing object shapes from partial inputs. However, most existing …

Point cloud completion aims to recover the complete shape based on a partial observation.
Existing methods require either complete point clouds or multiple partial observations of the …

Semantic scene completion is the task of jointly estimating 3D geometry and semantics of
objects and surfaces within a given extent. This is a particularly challenging task on real …