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
💡 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
💡 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 → 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
专业的3D数据处理与可视化界面
模块化设计,从底层到应用层的完整抽象
轻量级的点云查看器
基于COLMAP的完整3D重建流程
GPU加速的实时点云重建与融合
高性能的迭代最近点算法,支持多尺度配准
现代化的用户界面,功能强大且易用
智能的3D语义分割和标注功能
处理海量点云数据的语义标注,支持上亿点渲染
强大的3D数据选择和过滤工具,支持多种选择模式
精确的点云距离测量,支持实时标注和可视化
高精度角度测量,支持多点角度计算和标注
实时可视化3D机器学习模型训练和推理过程
3D深度学习模型推理结果的实时可视化展示
在Jupyter Notebook中交互式可视化3D数据
基于物理的渲染,支持材质、光照和阴影