gsply 0.2.1

Ultra-fast Gaussian Splatting PLY I/O library - pure Python with NumPy and Numba

gsply

Ultra-Fast Gaussian Splatting PLY I/O Library

93M Gaussians/sec read | 57M Gaussians/sec write | Auto-optimized

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What's New in v0.2.1

• Data Concatenation: GSData.concatenate() - Bulk merge (6.15x faster), GSData.add() - Optimized pairwise (1.9x faster)
• GPU Concatenation: GSTensor.add() - 18x faster than CPU
• Performance Optimization: make_contiguous() - Fix cache locality (2-45x speedup)
• Mask Management: Multi-layer boolean masks with GPU-optimized operations (100-1000x faster)

Full API Reference | Changelog

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Quick Start

from gsply import plyread, plywrite

# Read PLY file (auto-detects format, zero-copy)
data = plyread("model.ply")

# Access fields
positions = data.means    # (N, 3) xyz coordinates
colors = data.sh0         # (N, 3) RGB colors
scales = data.scales      # (N, 3) scale parameters
rotations = data.quats    # (N, 4) quaternions

# Write PLY file (automatically optimized)
plywrite("output.ply", data)

# Write compressed (71-74% smaller)
plywrite("output.ply", data, compressed=True)

Performance: 93M Gaussians/sec read, 57M Gaussians/sec write (400K Gaussians in 6-7ms)

Installation | Examples | API Reference | Performance

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Overview

Ultra-fast Gaussian Splatting PLY I/O for Python. Zero-copy reads, auto-optimized writes, optional GPU acceleration.

Key Features:

• Fast: 93M Gaussians/sec read, 57M Gaussians/sec write
• Auto-optimized: Writes are 2.6-2.8x faster automatically
• Pure Python: NumPy + Numba (no C++ compilation)
• Format support: Uncompressed PLY + PlayCanvas compressed (71-74% smaller)
• GPU ready: Optional PyTorch integration with GSTensor

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Installation

pip install gsply

Dependencies: NumPy and Numba (auto-installed)

Optional GPU acceleration:

pip install torch  # For GSTensor GPU features

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Examples

Basic I/O

from gsply import plyread, plywrite

# Read and access data
data = plyread("model.ply")
print(f"Loaded {len(data)} Gaussians")

# Unpack to individual arrays
means, scales, quats, opacities, sh0, shN = data.unpack()

# Write with individual arrays
plywrite("output.ply", means, scales, quats, opacities, sh0, shN)

# Or write directly from GSData
plywrite("output.ply", data)

Format Detection

from gsply import detect_format

is_compressed, sh_degree = detect_format("model.ply")
if is_compressed:
    print("Compressed PlayCanvas format")
else:
    print(f"Uncompressed format with SH degree {sh_degree}")

In-Memory Compression

from gsply import compress_to_bytes, decompress_from_bytes

# Compress for network transfer or storage
compressed_bytes = compress_to_bytes(data)

# Decompress from bytes
data_restored = decompress_from_bytes(compressed_bytes)

GPU Acceleration

from gsply import GSTensor

# Convert to GPU tensors (requires PyTorch)
gstensor = GSTensor.from_gsdata(data, device='cuda')

# Access GPU tensors
positions_gpu = gstensor.means  # torch.Tensor on GPU
colors_gpu = gstensor.sh0       # torch.Tensor on GPU

# Filter on GPU
high_opacity = gstensor[gstensor.opacities > 0.5]

# Convert back to CPU
data_cpu = gstensor.to_gsdata()

Data Manipulation

# Slicing and indexing
subset = data[100:200]          # Slice
first = data[0]                 # Single Gaussian
filtered = data[data.opacities > 0.5]  # Boolean mask

# Concatenation
combined = data1.add(data2)     # Pairwise (1.9x faster)
combined = data1 + data2        # Or use + operator
merged = GSData.concatenate([data1, data2, data3])  # Bulk (6.15x faster)

# Optimize for faster operations
data = data.make_contiguous()   # 2-45x speedup for operations

# Copy and modify
bright = data.copy()
bright.sh0 *= 1.5  # Make brighter

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Performance

Benchmark Summary

Uncompressed Format (400K Gaussians, SH0):

• Read: 5.7ms (70M Gaussians/sec)
• Write: 19.3ms (21M Gaussians/sec)

Compressed Format (400K Gaussians, SH0):

• Read: 8.5ms (47M Gaussians/sec)
• Write: 15.0ms (27M Gaussians/sec)
• Size reduction: 71-74%

Peak Performance:

• Read: 78M Gaussians/sec (1M Gaussians, SH0, uncompressed)
• Write: 29M Gaussians/sec (100K Gaussians, SH0, compressed)

GPU Transfer (400K Gaussians, RTX 3090 Ti):

• With optimization: 1.99ms (11.4x faster)
• Without optimization: 22.78ms

See detailed performance benchmarks for more information.

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Format Support

Uncompressed PLY

Standard binary little-endian PLY format:

SH Degree	Properties	Description
0	14	xyz, f_dc(3), opacity, scales(3), quats(4)
1	23	+ 9 f_rest coefficients
2	38	+ 24 f_rest coefficients
3	59	+ 45 f_rest coefficients

Compressed PLY (PlayCanvas)

Chunk-based quantized format:

• Automatically saves as .compressed.ply when compressed=True
• Compression ratio: 71-74% size reduction
• Compatible with PlayCanvas, SuperSplat, other WebGL viewers
• Parallel compression/decompression

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API Reference

Complete API documentation is available in docs/API_REFERENCE.md:

Core I/O:

• plyread(file_path) - Read PLY files
• plywrite(file_path, ...) - Write PLY files
• detect_format(file_path) - Detect format and SH degree

GSData Container:

• data.unpack() - Unpack to tuple
• data.to_dict() - Convert to dictionary
• data.copy() - Deep copy
• data.consolidate() - Optimize for slicing
• data[index] - Indexing and slicing

Compression:

• compress_to_bytes(data) - Compress to bytes
• compress_to_arrays(data) - Compress to arrays
• decompress_from_bytes(bytes) - Decompress from bytes

Utilities:

• sh2rgb(sh) - SH to RGB conversion
• rgb2sh(rgb) - RGB to SH conversion
• SH_C0 - Normalization constant

GPU Support (PyTorch):

• GSTensor.from_gsdata(data, device='cuda') - Convert to GPU
• gstensor.to_gsdata() - Convert to CPU
• Device management: .to(), .cpu(), .cuda()
• Precision: .half(), .float(), .double()
• Full slicing and manipulation support

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Development

Setup

# Clone repository
git clone https://github.com/OpsiClear/gsply.git
cd gsply

# Install in development mode
pip install -e .[dev]

# Run tests
pytest tests/ -v

# Run with coverage
pytest tests/ -v --cov=gsply --cov-report=html

Project Structure

gsply/
├── src/gsply/          # Source code
│   ├── gsdata.py       # GSData dataclass
│   ├── reader.py       # PLY reading
│   ├── writer.py       # PLY writing
│   ├── formats.py      # Format detection
│   └── torch/          # PyTorch integration
│       └── gstensor.py # GSTensor GPU dataclass
├── tests/              # Unit tests (169 tests)
├── benchmarks/         # Performance benchmarks
├── docs/               # Documentation
└── pyproject.toml      # Package configuration

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Testing

gsply has comprehensive test coverage with 169 passing tests:

# Run all tests
pytest tests/ -v

# Run PyTorch tests (requires torch)
pytest tests/ -v -k "torch or gstensor"

# Run with coverage
pytest tests/ -v --cov=gsply --cov-report=html

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Benchmarking

Compare gsply performance against other PLY libraries:

# Install benchmark dependencies
pip install -e .[benchmark]

# Run benchmark
python benchmarks/benchmark.py

# Custom settings
python benchmarks/benchmark.py --config.file path/to/model.ply --config.iterations 20

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Documentation

• API Reference - Complete API documentation
• Changelog - Version history and release notes
• Contributing - Contribution guidelines

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CI/CD

Complete GitHub Actions pipeline:

• Multi-platform: Ubuntu, Windows, macOS
• Multi-version: Python 3.10, 3.11, 3.12, 3.13
• Core + PyTorch testing
• Automated benchmarking
• PyPI publishing on release

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Contributing

Contributions are welcome! Please see .github/CONTRIBUTING.md for guidelines.

Quick start:

1. Fork the repository
2. Create a feature branch
3. Make your changes with tests
4. Run tests and benchmarks
5. Submit a pull request

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License

MIT License - see LICENSE file for details.

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Citation

If you use gsply in your research, please cite:

@software{gsply2024,
  author = {OpsiClear},
  title = {gsply: Ultra-Fast Gaussian Splatting PLY I/O},
  year = {2024},
  url = {https://github.com/OpsiClear/gsply}
}

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Related Projects

• gsplat: CUDA-accelerated Gaussian Splatting rasterizer
• nerfstudio: NeRF training framework with Gaussian Splatting support
• PlayCanvas SuperSplat: Web-based Gaussian Splatting viewer
• 3D Gaussian Splatting: Original paper and implementation

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Made with Python and numpy

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