Advanced Topics#

Advanced usage, optimization, and customization techniques.

Advanced tutorials are under development:

CUDA Programming

GPU acceleration for point cloud processing
Custom CUDA kernels
Memory management on GPU
Performance profiling

Custom Operators

Implementing custom algorithms
Extending CloudViewer with C++
Creating Python bindings
Plugin development

Performance Optimization

Multi-threading strategies
Memory optimization techniques
Large-scale point cloud handling
Benchmark and profiling tools

Current Resources#

For advanced topics, refer to:

C++ Development

C++ documentation - Complete C++ API documentation
Building from Source - Building from source
Contributing to ACloudViewer - Contributing guidelines

Performance Tips

Use efficient data structures:

import cloudViewer as cv3d

# Downsample for faster processing
pcd_down = pcd.voxel_down_sample(voxel_size=0.05)

# Use KD-tree for nearest neighbor searches
kdtree = cv3d.geometry.KDTreeFlann(pcd_down)

# Multi-threading (automatically enabled)
pcd.estimate_normals()  # Uses OpenMP

Large Point Clouds

# Process in chunks
def process_large_cloud(filename, chunk_size=1000000):
    pcd = cv3d.io.read_point_cloud(filename)

    # Downsample first
    pcd_down = pcd.voxel_down_sample(0.01)

    # Process in batches if still too large
    if len(pcd_down.points) > chunk_size:
        # Split and process
        pass