Modern 3D Data Processing System
Professional Point Cloud & Mesh Processing | Big Data Support | Cross-Platform Solution
ACloudViewer is an open-source 3D point cloud and triangular mesh processing software library. It supports rapid development of software for processing 3D data, highly based on CloudCompare, Open3D, ParaView and COLMAP, and integrates the PCL library.
Originally designed to compare two 3D point clouds (such as those obtained by laser scanning) or the difference between point clouds and triangular meshes. It relies on an octree structure highly optimized for this specific use case, capable of handling massive point cloud data (typically over 10 million points, up to 120 million points with 2GB memory).
Powerful 3D data structures and processing algorithms, supporting point clouds, meshes and various geometries
COLMAP-based scene reconstruction system, supporting complete workflow from images to 3D models
High-precision point cloud registration algorithms, including ICP, RANSAC and other methods
High-performance rendering engine based on VTK and OpenGL, supporting PBR physical rendering
Integrated with PyTorch and TensorFlow, supporting 3D deep learning applications
GPU acceleration for core 3D operations, supporting CUDA 12.x
Provides C++ and Python dual-language API, flexible and easy to use
Rich plugin ecosystem, supporting custom feature extensions
All current and past release downloads are available on GitHub releases.
Download the .whl file for your system and Python version from GitHub Releases
๐ก Due to file size exceeding PyPI limits, manual download is required
pip install cloudviewer-*.whl
Example: pip install cloudviewer-3.9.3-cp310-cp310-win_amd64.whl
Supports Python 3.10-3.12 | Ubuntu 20.04+, macOS 10.15+, Windows 10+ (64-bit)
Download the corresponding .whl file from GitHub Releases, then install:
pip install cloudviewer-*.whl
๐ก Due to large file size, direct PyPI installation is not supported
python -c "import cloudViewer as cv3d; print(cv3d.__version__)"
import cloudViewer as cv3d
# Create sphere mesh
mesh = cv3d.geometry.ccMesh.create_sphere()
mesh.compute_vertex_normals()
# Visualize
cv3d.visualization.draw(mesh, raw_mode=True)
git clone --recursive https://github.com/Asher-1/ACloudViewer.git
cd ACloudViewer
mkdir build && cd build
cmake ..
make -j$(nproc)
./bin/ACloudViewer
For detailed compilation instructions, please refer to BUILD.md
Select the installer for your system from the Download section
Double-click the desktop icon or launch ACloudViewer from the Start Menu
File โ Open to select your point cloud or mesh file
Supported formats: PLY, PCD, LAS, LAZ, E57, OBJ, STL, FBX, etc.
Explore ACloudViewer's powerful applications in different fields
Professional 3D data processing and visualization interface
Modular design, complete abstraction from bottom layer to application layer
Lightweight point cloud viewer
Complete 3D reconstruction workflow based on COLMAP
GPU-accelerated real-time point cloud reconstruction and fusion
High-performance iterative closest point algorithm, supporting multi-scale registration
Modern user interface, powerful and easy to use
Intelligent 3D semantic segmentation and annotation
Semantic annotation for massive point cloud data, supporting rendering of hundreds of millions of points
Powerful 3D data selection and filtering tools with multiple selection modes
Precise point cloud distance measurement with real-time annotation and visualization
High-precision angle measurement supporting multi-point angle calculation and annotation
Real-time visualization of 3D machine learning model training and inference
Real-time visualization of 3D deep learning model inference results
Interactive 3D data visualization in Jupyter Notebook
Physics-based rendering, supporting materials, lighting and shadows