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 self-driving cars, robotics, and remote sensing have widened the
range of applications for 3D Point Cloud (PC) data. This data format poses several new …

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 …

Computer graphics, 3D computer vision and robotics communities have produced multiple
approaches to representing 3D geometry for rendering and reconstruction. These provide …

In this paper, we propose a Point Fractal Network (PF-Net), a novel learning-based
approach for precise and high-fidelity point cloud completion. Unlike existing point cloud …

LiDAR point cloud analysis is a core task for 3D computer vision, especially for autonomous
driving. However, due to the severe sparsity and noise interference in the single sweep …

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 …

Shape completion, the problem of estimating the complete geometry of objects from partial
observations, lies at the core of many vision and robotics applications. In this work, we …

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 …

We introduce a method for learning to generate the surface of 3D shapes. Our approach
represents a 3D shape as a collection of parametric surface elements and, in contrast to …