摘要随着传感器技术的不断发展, 三维点云被广泛应用于自动驾驶, 机器人, 遥感, 文物修复,
增强现实, 虚拟现实等领域的视觉任务中. 然而, 直接应用收集到的海量原始点云数据得到的效果 …

Most existing point cloud upsampling methods have roughly three steps: feature extraction,
feature expansion and 3D coordinate prediction. However, they usually suffer from two …

Point cloud upsampling aims to generate dense and uniformly distributed point sets from a
sparse point cloud, which plays a critical role in 3D computer vision. Previous methods …

Multi-Beam LiDAR (MBL) sensors sample the real-world with discrete 3D point clouds (PC)
and have become a major and essential 3D sensing capability for autonomous robots. To …

We propose SparseDC, a model for Depth Completion from Sparse and non-uniform inputs.
Unlike previous methods focusing on completing fixed distributions on benchmark datasets …

Despite the success of recent upsampling approaches generating high-resolution point sets
with uniform distribution and meticulous structures is still challenging. Unlike existing …

Point cloud upsampling is crucial for 3D reconstruction, with recent research significantly
benefitting from the advances in deep learning technologies. The majority of existing …

Unpaired point cloud completion involves filling in missing parts of a point cloud without
requiring partial-complete correspondence. Meanwhile, since point cloud completion is an ill …

In this study, we propose a novel self-supervised approach for point cloud upsampling,
integrating a pre-trained denoising phase to enhance the quality and accuracy of the …

Developing a unified multi-task foundation model has become a critical challenge in
computer vision research. In the current field of 3D computer vision, most datasets solely …