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 …

The inversion of real images into StyleGAN's latent space is a well-studied problem.
Nevertheless, applying existing approaches to real-world scenarios remains an open …

Abstract Neural Radiance Fields (NeRFs) offers a state-of-the-art quality in synthesizing
novel views of complex 3D scenes from a small subset of base images. For NeRFs to …

Point cloud completion is able to estimate the complete point cloud starting from the missing
point cloud, which obtains higher quality point cloud data for widely used in remote sensing …

Point cloud completion is the task of producing a complete 3D shape given an input of a
partial point cloud. It has become a vital process in 3D computer graphics, vision and …

Outliers will inevitably creep into the captured point cloud during 3D scanning, degrading
cutting-edge models on various geometric tasks heavily. This paper looks at an intriguing …

Most existing point cloud completion methods are only applicable to partial point clouds
without any noises and outliers, which does not always hold in practice. We propose in this …

Recently introduced implicit field representations offer an effective way of generating 3D
object shapes. They leverage implicit decoder trained to take a 3D point coordinate …

Point cloud completion aims to acquire complete and high-fidelity point clouds from partial
and low-quality point clouds, which are used in remote sensing applications. Existing …

Abstract Three-dimensional (3D) point cloud analysis has become one of the attractive
subjects in realistic imaging and machine visions due to its simplicity, flexibility and powerful …