gsply 0.2.13
Ultra-fast Gaussian Splatting PLY I/O library - pure Python with NumPy and Numba. Features 11x faster GPU transfers and automatic format state tracking for seamless conversions.
Requires Python: >=3.10
Keywords: gaussian-splatting, ply, 3d, point-cloud, compression, nerf, 3dgs

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# gsply

### Ultra-Fast Gaussian Splatting PLY I/O Library

[![Python](https://img.shields.io/badge/python-3.10%2B-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)
[![Documentation](https://readthedocs.org/projects/gsply/badge/?version=latest)](https://gsply.readthedocs.io/)
[![Tests](https://img.shields.io/badge/tests-406%20passing-brightgreen.svg)](#testing)

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

[Quick Start](#quick-start) • [Installation](#installation) • [Examples](#examples) • [API Reference](docs/API_REFERENCE.md) • [Performance](#performance)

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---

## Quick Start

```python
from gsply import plyread, GSData, GSTensor

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

# Or use object-oriented API
data = GSData.load("model.ply")  # Classmethod

# 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

# Save PLY file
data.save("output.ply")  # Uncompressed
data.save("output.ply", compressed=True)  # Compressed (71-74% smaller)

# GPU acceleration (optional)
gstensor = GSTensor.load("model.ply", device='cuda')
gstensor.save("output.compressed.ply")  # GPU compression
```

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

---

## Overview

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

### Key Features

- **Ultra-Fast**: 93M Gaussians/sec read, 57M Gaussians/sec write
- **Zero-Copy**: Reads use memory views for maximum performance
- **Auto-Optimized**: Writes are 2.6-2.8x faster automatically
- **Format Support**: Uncompressed PLY + PlayCanvas compressed (71-74% smaller) + SOG format
- **GPU Ready**: Optional PyTorch integration with GSTensor (11x faster transfers)
- **Pure Python**: NumPy + Numba (no C++ compilation required)
- **Object-Oriented API**: `data.save()`, `GSData.load()`, `gstensor.save()`, `GSTensor.load()`
- **Format Conversion**: `normalize()`, `denormalize()` with fused kernels (~8-15x faster)
- **Color Conversion**: `to_rgb()`, `to_sh()` for SH ↔ RGB conversion
- **Comprehensive**: 406 passing tests, full type hints, extensive documentation

---

## Installation

### Basic Installation

```bash
pip install gsply
```

**Core dependencies:** NumPy and Numba (automatically installed)

### Optional Features

**GPU Acceleration (PyTorch):**
```bash
pip install torch
```
Enables `GSTensor`, `plyread_gpu()`, `plywrite_gpu()`, and GPU-accelerated format conversions.

**SOG Format Support:**
```bash
pip install gsply[sogs]
```
Enables `sogread()` for reading SOG (Splat Ordering Grid) format files.

**Full Installation:**
```bash
pip install gsply[sogs] torch  # GPU + SOG support
```

**Development:**
```bash
git clone https://github.com/OpsiClear/gsply.git
cd gsply
pip install -e .[dev]  # Includes pytest, ruff, mypy
```

---

## Examples

### Basic I/O

```python
from gsply import plyread, plywrite, GSData

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

# Object-oriented API
data = GSData.load("model.ply")  # Auto-detects format
data.save("output.ply")  # Uncompressed
data.save("output.ply", compressed=True)  # Compressed

# 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)
```

### Format Conversion

```python
from gsply import GSData
import numpy as np

# Load PLY file (log-scales, logit-opacities)
data = GSData.load("scene.ply")

# Convert to linear format for easier manipulation
data.denormalize()  # Uses fused kernel (~8-15x faster)

# Modify in linear space
data.opacities = np.clip(data.opacities * 1.2, 0, 1)
data.scales *= 1.1

# Convert back to PLY format before saving
data.normalize()  # Uses fused kernel (~8-15x faster)
data.save("modified.ply")
```

### GPU Acceleration

```python
from gsply import GSTensor, plyread_gpu, plywrite_gpu

# Direct GPU I/O (4-5x faster than CPU decompress + GPU transfer)
gstensor = plyread_gpu("model.compressed.ply", device='cuda')

# Or convert from CPU
data = GSData.load("model.ply")
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]

# GPU format conversion
gstensor.denormalize()  # GPU-accelerated

# Write back (GPU compression)
gstensor.save("output.compressed.ply")  # GPU compression by default
```

### Creating from External Data

```python
from gsply import GSData, GSTensor
import numpy as np
import torch

# Create GSData from arrays with format preset
data = GSData.from_arrays(
    means=np.random.randn(1000, 3),
    scales=np.random.rand(1000, 3),
    quats=np.random.randn(1000, 4),
    opacities=np.random.rand(1000),
    sh0=np.random.randn(1000, 3),
    format="linear"  # or "auto", "ply", "rasterizer"
)

# Create GSTensor from tensors
gstensor = GSTensor.from_arrays(
    means=torch.randn(1000, 3),
    scales=torch.rand(1000, 3),
    quats=torch.randn(1000, 4),
    opacities=torch.rand(1000),
    sh0=torch.randn(1000, 3),
    format="linear",
    device="cuda"
)
```

### Data Manipulation

```python
from gsply import GSData

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

# Concatenation
combined = data1 + data2                 # Pairwise (1.9x faster)
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
```

### In-Memory Compression

```python
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)
```

### SOG Format (Optional)

```python
from gsply import sogread

# Read SOG file - returns GSData (same API as plyread)
data = sogread("model.sog")
positions = data.means
colors = data.sh0

# Read from bytes (in-memory, no disk I/O)
with open("model.sog", "rb") as f:
    sog_bytes = f.read()
data = sogread(sog_bytes)  # Fully in-memory extraction
```

---

## 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 `_base` optimization: 1.99ms (11x faster, single tensor transfer)
- Without `_base`: 22.78ms (CPU copy + transfer)

**Format Conversion (Fused Kernels):**
- `normalize()` / `denormalize()`: ~8-15x faster with parallel Numba kernels
- Single-pass processing reduces memory overhead

See detailed [performance benchmarks](docs/API_REFERENCE.md#performance) for more information.

---

## 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 with Numba JIT

### SOG Format (Splat Ordering Grid) - Optional

WebP-based texture format for web deployment:
- Requires `gsply[sogs]` installation
- Uses WebP images for efficient storage
- Codebook-based compression for scales and colors
- Compatible with PlayCanvas splat-transform
- Supports both `.sog` ZIP bundles and folder formats
- Returns `GSData` container (same API as `plyread()`)
- In-memory ZIP extraction: Can read directly from bytes

---

## API Reference

Complete API documentation: [docs/API_REFERENCE.md](docs/API_REFERENCE.md)

### Core I/O
- `plyread(file_path)` - Read PLY files (auto-detects format)
- `plywrite(file_path, ...)` - Write PLY files
- `detect_format(file_path)` - Detect format and SH degree
- `sogread(file_path | bytes)` - Read SOG files (optional)

### GSData Container
- `GSData.load(file_path)` - Load from PLY (classmethod)
- `data.save(file_path, compressed=False)` - Save to PLY
- `GSData.from_arrays(...)` - Create from arrays with format preset
- `GSData.from_dict(...)` - Create from dictionary with format preset
- `data.normalize()` / `data.denormalize()` - Format conversion (fused kernels, ~8-15x faster)
- `data.to_rgb()` / `data.to_sh()` - Color conversion
- Format query: `is_scales_ply`, `is_scales_linear`, `is_opacities_ply`, `is_opacities_linear`, `is_sh0_sh`, `is_sh0_rgb`, `is_sh_order_0/1/2/3`
- `data[index]` - Indexing and slicing
- `data.unpack()` - Unpack to tuple
- `data.copy()` - Deep copy

### Compression APIs
- `compress_to_bytes(data)` - Compress to bytes
- `compress_to_arrays(data)` - Compress to arrays
- `decompress_from_bytes(bytes)` - Decompress from bytes

### Utility Functions
- `sh2rgb(sh)` / `rgb2sh(rgb)` - Color conversion
- `logit(x)` / `sigmoid(x)` - Optimized CPU functions
- `apply_pre_activations(data, ...)` - Fused activation kernel (~8-15x faster)
- `apply_pre_deactivations(data, ...)` - Fused deactivation kernel (~8-15x faster)
- `SH_C0` - Normalization constant

### GPU Support (PyTorch)
- `GSTensor.load(file_path, device='cuda')` - Load from PLY (classmethod)
- `gstensor.save(file_path, compressed=True)` - Save with GPU compression
- `GSTensor.from_arrays(...)` - Create from tensors with format preset
- `GSTensor.from_gsdata(data, device='cuda')` - Convert to GPU
- `gstensor.to_gsdata()` - Convert to CPU
- `gstensor.normalize()` / `gstensor.denormalize()` - GPU format conversion
- `gstensor.to_rgb()` / `gstensor.to_sh()` - GPU color conversion
- Format query: `is_scales_ply`, `is_scales_linear`, `is_opacities_ply`, `is_opacities_linear`, `is_sh0_sh`, `is_sh0_rgb`, `is_sh_order_0/1/2/3`
- Device management: `.to()`, `.cpu()`, `.cuda()`
- Precision: `.half()`, `.float()`, `.double()`

---

## What's New

### v0.2.11 - GPU Compression Optimization

- **torch.compile() Auto-Optimization**: GPU compression now uses `torch.compile()` when available
  - ~25% faster GPU compression (5.0ms → 4.0ms for 365K Gaussians)
  - Automatic fallback to eager mode if compilation fails
  - Zero configuration required - works transparently
- **GPU Rounding Fix**: Fixed quaternion quantization to match CPU behavior exactly
- **Platform Support**: Triton backend on Linux (standard), Windows (requires `triton-windows` package)

### v0.2.9 - Protocol Interfaces & Performance Optimization

- **Protocol Interfaces**: Type-safe interfaces for format management
  - `FormatAware` - Protocol for objects that track format state
  - `Normalizable` - Protocol for objects that support format conversion
  - `GaussianContainer` - Protocol for objects containing Gaussian splat data
  - Enables structural typing across GSData and GSTensor
- **Format Management API**: Advanced format control methods
  - `format_state` property - Returns current format as immutable dictionary
  - `copy_format_from(other)` - Copy format state from another object
  - `with_format(**kwargs)` - Create shallow copy with modified format
- **Performance Optimizations**: Improved efficiency for common operations
  - Removed auto-consolidate overhead in plywrite() - users can call `make_contiguous()` manually when needed
  - In-place format conversion now default (`inplace=True`) - reduces memory allocations
  - Better performance for already-contiguous data

### v0.2.8 - Format Query Properties

- **Format Query Properties**: Convenient boolean properties to check current data format
  - `is_scales_ply`, `is_scales_linear` - Check scale format
  - `is_opacities_ply`, `is_opacities_linear` - Check opacity format
  - `is_sh0_sh`, `is_sh0_rgb` - Check color format
  - `is_sh_order_0/1/2/3` - Check SH degree
  - Available on both `GSData` and `GSTensor`

### v0.2.7 - Fused Activation Kernels & Performance Optimization

- **Fused Activation Kernels**: Ultra-fast format conversion with parallel Numba kernels
  - `apply_pre_activations()` - Fused kernel for activating scales, opacities, and quaternions (~8-15x faster)
  - `apply_pre_deactivations()` - Fused kernel for deactivating scales and opacities (~8-15x faster)
  - `normalize()` and `denormalize()` now use optimized fused kernels internally
  - Single-pass processing reduces memory overhead and improves cache locality

### v0.2.6 - Format Safety & Auto-detection

- **Convenience Factory Methods**: `GSData.from_arrays()`, `GSData.from_dict()`, `GSTensor.from_arrays()`, `GSTensor.from_dict()`
- **Auto-Format Detection**: Smart heuristics automatically detect PLY vs Linear format
- **Format Safety**: Strict validation prevents mixing incompatible formats
- **Format Helpers**: `create_ply_format()`, `create_rasterizer_format()`

### v0.2.5 - SOG Format Support & API Improvements

- **SOG Format Support**: `sogread()` - Read SOG (Splat Ordering Grid) format files
- **Object-Oriented I/O API**: `data.save()`, `GSData.load()`, `gstensor.save()`, `GSTensor.load()`
- **Format Conversion API**: `normalize()`, `denormalize()` for linear ↔ PLY format conversion
- **Color Conversion API**: `to_rgb()`, `to_sh()` for SH ↔ RGB conversion

[Full Changelog](docs/CHANGELOG.md) • [API Reference](docs/API_REFERENCE.md)

---

## Development

### Setup

```bash
# 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
│   ├── utils.py        # Utility functions (fused kernels)
│   └── torch/          # PyTorch integration
│       └── gstensor.py # GSTensor GPU dataclass
├── tests/              # Unit tests (365 tests)
├── benchmarks/         # Performance benchmarks
├── docs/               # Documentation
└── pyproject.toml      # Package configuration
```

### Testing

gsply has comprehensive test coverage with **406 passing tests**:

```bash
# 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
```

### Benchmarking

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

# Run benchmark
python benchmarks/benchmark.py
```

---

## Documentation

- **[API Reference](docs/API_REFERENCE.md)** - Complete API documentation
- **[Changelog](docs/CHANGELOG.md)** - Version history and release notes
- **[Contributing](docs/CONTRIBUTING.md)** - Contribution guidelines

---

## Contributing

Contributions are welcome! Please see [docs/CONTRIBUTING.md](docs/CONTRIBUTING.md) for guidelines.

**Quick start:**
1. Fork the repository
2. Create a feature branch
3. Make your changes with tests
4. Run tests: `pytest tests/ -v`
5. Submit a pull request

---

## License

MIT License - see [LICENSE](LICENSE) file for details.

---

## Citation

If you use gsply in your research, please cite:

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

---

## 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**

[Report Bug](https://github.com/OpsiClear/gsply/issues) • [Request Feature](https://github.com/OpsiClear/gsply/issues) • [Documentation](docs/API_REFERENCE.md)

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