Convert Figma logo to code with AI

CloudCompare logoCloudCompare

CloudCompare main repository

3,977
1,092
3,977
261

Top Related Projects

4,974

WebGL point cloud viewer for large datasets

10,527

Point Cloud Library (PCL)

1,248

PDAL is Point Data Abstraction Library. GDAL for point cloud data.

3,156

3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK)

Quick Overview

CloudCompare is an open-source 3D point cloud and mesh processing software. It provides a comprehensive set of tools for editing, rendering, and analyzing 3D point cloud data and triangular meshes. CloudCompare is widely used in various fields such as surveying, geology, archaeology, and computer vision.

Pros

  • Extensive set of tools for point cloud and mesh processing
  • Cross-platform compatibility (Windows, macOS, Linux)
  • Active community and regular updates
  • Free and open-source

Cons

  • Steep learning curve for beginners
  • Limited documentation for advanced features
  • Performance can be slow with very large datasets
  • User interface may feel outdated compared to some commercial alternatives

Getting Started

To get started with CloudCompare:

  1. Download the latest version from the official website: https://www.cloudcompare.org/release/
  2. Install the software following the instructions for your operating system.
  3. Launch CloudCompare and open a point cloud or mesh file using File > Open.
  4. Explore the various tools and features available in the toolbar and menus.
  5. For detailed instructions and tutorials, refer to the official wiki: https://www.cloudcompare.org/doc/wiki/index.php?title=Main_Page

Note: CloudCompare is primarily a GUI-based application and does not provide a programming API or library for direct code integration. Therefore, code examples are not applicable in this context.

Competitor Comparisons

4,974

WebGL point cloud viewer for large datasets

Pros of Potree

  • Web-based visualization, allowing for easy sharing and access through browsers
  • Optimized for streaming and rendering large point cloud datasets
  • Supports various point cloud formats and offers a range of visualization options

Cons of Potree

  • Limited editing and analysis capabilities compared to CloudCompare
  • Requires web server setup and configuration for deployment
  • May have performance limitations for extremely large datasets on low-end devices

Code Comparison

Potree (JavaScript):

Potree.loadPointCloud("pointcloud.las", "PointCloud", function(e){
    viewer.scene.addPointCloud(e.pointcloud);
    viewer.fitToScreen();
});

CloudCompare (C++):

ccPointCloud* cloud = ccPointCloud::From(file);
if (cloud) {
    m_app->addToDB(cloud);
    m_app->setSelectedInDB(cloud, true);
}

Potree focuses on web-based rendering and uses JavaScript for point cloud loading and visualization. CloudCompare, being a desktop application, utilizes C++ for more comprehensive point cloud processing and analysis capabilities.

10,527

Point Cloud Library (PCL)

Pros of PCL

  • More extensive library with a wider range of algorithms and tools for point cloud processing
  • Better suited for integration into larger software projects due to its modular design
  • Stronger support for real-time processing and robotics applications

Cons of PCL

  • Steeper learning curve and more complex API compared to CloudCompare
  • Less user-friendly for non-programmers or those seeking quick visualizations
  • Requires more setup and configuration for basic tasks

Code Comparison

PCL:

#include <pcl/point_cloud.h>
#include <pcl/io/pcd_io.h>
#include <pcl/visualization/cloud_viewer.h>

pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl::io::loadPCDFile ("input.pcd", *cloud);

CloudCompare:

#include <CCCoreLib/CloudSamplingTools.h>
#include <ccPointCloud.h>

ccPointCloud* cloud = new ccPointCloud();
cloud->loadFromFile("input.pcd");

Both libraries offer powerful tools for point cloud processing, but PCL is more comprehensive and flexible for advanced applications, while CloudCompare provides a more accessible interface for basic tasks and visualizations. The choice between them depends on the specific project requirements and user expertise.

1,248

PDAL is Point Data Abstraction Library. GDAL for point cloud data.

Pros of PDAL

  • Command-line focused, enabling easier automation and scripting
  • Extensive support for various LiDAR formats and processing algorithms
  • Better integration with other geospatial tools and libraries

Cons of PDAL

  • Steeper learning curve for non-programmers
  • Less intuitive for quick, interactive point cloud visualization
  • Limited GUI options compared to CloudCompare

Code Comparison

PDAL (pipeline.json):

{
  "pipeline": [
    "input.las",
    {
      "type": "filters.outlier",
      "method": "statistical",
      "mean_k": 8,
      "multiplier": 2.0
    },
    "output.las"
  ]
}

CloudCompare (Python script):

import cc

cloud = cc.loadPointCloud("input.las")
cloud.computeNormals()
cloud.removeOutliers(8, 2.0, cc.STATISTICAL_OUTLIER_REMOVAL)
cc.savePointCloud(cloud, "output.las")

Both examples demonstrate outlier removal, but PDAL uses a JSON pipeline configuration, while CloudCompare relies on a Python script using its API.

3,156

3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK)

Pros of PyVista

  • Python-based, allowing for easier integration with data science workflows and libraries
  • Extensive documentation and tutorials, making it more accessible for beginners
  • Seamless integration with NumPy arrays for efficient data manipulation

Cons of PyVista

  • Limited built-in analysis tools compared to CloudCompare's extensive feature set
  • Steeper learning curve for users not familiar with Python programming
  • Less optimized for handling extremely large point cloud datasets

Code Comparison

CloudCompare (C++):

ccPointCloud* cloud = new ccPointCloud("MyCloud");
cloud->reserve(1000);
for (int i = 0; i < 1000; ++i) {
    cloud->addPoint(CCVector3(rand(), rand(), rand()));
}

PyVista (Python):

import pyvista as pv
import numpy as np

points = np.random.rand(1000, 3)
cloud = pv.PolyData(points)

Both examples create a point cloud with 1000 random points, but PyVista's implementation is more concise and leverages NumPy for efficient array operations.

Convert Figma logo designs to code with AI

Visual Copilot

Introducing Visual Copilot: A new AI model to turn Figma designs to high quality code using your components.

Try Visual Copilot

README

CloudCompare

Homepage: https://cloudcompare.org

GitHub release

Build

Introduction

CloudCompare is a 3D point cloud (and triangular mesh) processing software. It was originally designed to perform comparison between two 3D points clouds (such as the ones obtained with a laser scanner) or between a point cloud and a triangular mesh. It relies on an octree structure that is highly optimized for this particular use-case. It was also meant to deal with huge point clouds (typically more than 10 million points, and up to 120 million with 2 GB of memory).

More on CloudCompare here

License

This project is under the GPL license: https://www.gnu.org/licenses/gpl-3.0.html

This means that you can use it as is for any purpose. But if you want to distribute it, or if you want to reuse its code or part of its code in a project you distribute, you have to comply with the GPL license. In effect, all the code you mix or link with CloudCompare's code must be made public as well. This code cannot be used in a closed source software.

Installation

Linux:

Compilation

Supports: Windows, Linux, and macOS

Refer to the BUILD.md file for up-to-date information.

Basically, you have to:

  • clone this repository
  • install mandatory dependencies (OpenGL, etc.) and optional ones if you really need them (mainly to support particular file formats, or for some plugins)
  • launch CMake (from the trunk root)
  • enjoy!

Contributing to CloudCompare

If you want to help us improve CloudCompare or create a new plugin you can start by reading this guide

Supporting the project

If you want to help us in another way, you can make donations via donorbox

Thanks!