Converting Sparse Point Clouds to Dense, Smooth Surfaces

I have a sparse point cloud, and I want get the dense and smoothy surface point cloud.

To convert a sparse point cloud into a dense and smooth surface point cloud, you can follow these general steps:

1. Preprocessing:

   • Noise Removal: Clean the point cloud by removing outliers and noise. Tools like Statistical Outlier Removal (SOR) or Radius Outlier Removal (ROR) can be used.
   • Downsampling: If the point cloud is too large, you might want to downsample it to make the processing faster. Voxel Grid filtering is a common method for this.

2. Surface Reconstruction:

   • Poisson Surface Reconstruction: This method can generate a smooth surface from a point cloud. It works well for creating watertight surfaces.
   • Ball Pivoting Algorithm (BPA): This method is useful for reconstructing surfaces from point clouds by rolling a virtual ball over the points.
   • Delaunay Triangulation: This method can be used to create a mesh from the point cloud, which can then be smoothed.

3. Mesh Refinement:

   • Smoothing: Apply smoothing algorithms like Laplacian smoothing or Taubin smoothing to the mesh to make the surface smoother.
   • Subdivision: Use subdivision techniques like Loop or Catmull-Clark to increase the mesh density and smoothness.

4. Postprocessing:

   • Mesh Simplification: If the resulting mesh is too dense, you can simplify it using methods like Quadric Edge Collapse Decimation.
   • Texture Mapping: If you need a textured surface, you can apply texture mapping techniques.

Tools and Libraries

• PCL (Point Cloud Library): Offers a wide range of tools for point cloud processing, including filtering, surface reconstruction, and smoothing.
• Meshlab: A powerful tool for processing and editing 3D triangular meshes. It includes many algorithms for surface reconstruction and smoothing.
• Open3D: A modern library for 3D data processing, which includes tools for point cloud processing and surface reconstruction.
• Blender: A versatile 3D modeling software that can be used for mesh refinement and smoothing.

Example Workflow Using PCL and Open3D

Using PCL (C++):

#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/filters/statistical_outlier_removal.h>
#include <pcl/surface/poisson.h>
#include <pcl/surface/mls.h>

int main() {
    // Load point cloud
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
    pcl::io::loadPCDFile("input.pcd", *cloud);

    // Noise removal
    pcl::StatisticalOutlierRemoval<pcl::PointXYZ> sor;
    sor.setInputCloud(cloud);
    sor.setMeanK(50);
    sor.setStddevMulThresh(1.0);
    sor.filter(*cloud);

    // Moving Least Squares (MLS) for upsampling and smoothing
    pcl::PointCloud<pcl::PointNormal>::Ptr mls_points(new pcl::PointCloud<pcl::PointNormal>);
    pcl::MovingLeastSquares<pcl::PointXYZ, pcl::PointNormal> mls;
    mls.setInputCloud(cloud);
    mls.setSearchRadius(0.03);
    mls.setPolynomialFit(true);
    mls.setComputeNormals(true);
    mls.process(*mls_points);

    // Poisson surface reconstruction
    pcl::Poisson<pcl::PointNormal> poisson;
    poisson.setInputCloud(mls_points);
    pcl::PolygonMesh mesh;
    poisson.reconstruct(mesh);

    // Save the mesh
    pcl::io::savePLYFile("output.ply", mesh);

    return 0;
}

Using Open3D (Python):

import open3d as o3d

# Load point cloud
pcd = o3d.io.read_point_cloud("input.pcd")

# Noise removal
pcd, ind = pcd.remove_statistical_outlier(nb_neighbors=20, std_ratio=2.0)

# Surface reconstruction using Poisson
pcd.estimate_normals()
mesh, densities = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(pcd, depth=9)

# Save the mesh
o3d.io.write_triangle_mesh("output.ply", mesh)

These examples demonstrate basic workflows for converting a sparse point cloud into a dense and smooth surface point cloud. You can adjust parameters and methods based on your specific requirements and the characteristics of your point cloud.