Point Cloud Super Resolution
10 papers with code • 2 benchmarks • 1 datasets
Point cloud super-resolution is a fundamental problem for 3D reconstruction and 3D data understanding. It takes a low-resolution (LR) point cloud as input and generates a high-resolution (HR) point cloud with rich details
SHREC
Latest papers with no code
Diffusion-Based Point Cloud Super-Resolution for mmWave Radar Data
The millimeter-wave radar sensor maintains stable performance under adverse environmental conditions, making it a promising solution for all-weather perception tasks, such as outdoor mobile robotics.
PDF: Point Diffusion Implicit Function for Large-scale Scene Neural Representation
That is, the sampling space is reduced from the unbounded space to the scene surface.
Frequency-Selective Mesh-to-Mesh Resampling for Color Upsampling of Point Clouds
Secondly, we propose to apply a novel Frequency-Selective Mesh-to-Mesh Resampling (FSMMR) technique for the interpolation of the points in 2D.
