staysift 0.1.0

GPU-resident SIFT with zero-copy DLPack handoff -- pure Python, OpenCV-accurate, faster end-to-end

PySIFT

GPU-Resident Deterministic SIFT for Deep Learning Vision Pipelines

A pure-Python, GPU-resident SIFT implementation that matches OpenCV SIFT accuracy while running 26% faster end-to-end with 4x matching speedup. Zero-copy DLPack interop keeps tensors on the GPU across the full pipeline -- no PCIe round-trips.

Architecture

Key Features

• GPU-resident pipeline -- Detection, description, matching, RANSAC, and blending all execute on the GPU via CuPy + Numba CUDA kernels
• Zero-copy DLPack handoff -- CuPy arrays pass to PyTorch tensors without memory copies, enabling seamless integration with deep learning pipelines
• OpenCV-accurate -- Numerically equivalent to OpenCV SIFT (Lowe 2004), verified across HPatches, Oxford 5K, IMC Phototourism, and MegaDepth-1500
• Modular descriptor/matcher backends -- Swap in HardNet, HyNet (learned descriptors) or LightGlue (learned matching) with a single config flag
• Deterministic -- Bitwise reproducible results via warp-shuffle reductions (no atomicAdd non-determinism)

Qualitative Results

Installation

Prerequisites: CUDA dependencies

PySIFT requires an NVIDIA GPU with CUDA. Two dependencies must be installed manually because they are CUDA-version-specific:

# Check your CUDA version
nvcc --version

# 1. Install CuPy (pick ONE matching your CUDA version)
pip install cupy-cuda12x   # CUDA 12.x
pip install cupy-cuda11x   # CUDA 11.x

# 2. Install PyTorch with CUDA (default pip installs CPU-only!)
pip install torch --index-url https://download.pytorch.org/whl/cu124   # CUDA 12.4
pip install torch --index-url https://download.pytorch.org/whl/cu121   # CUDA 12.1
pip install torch --index-url https://download.pytorch.org/whl/cu118   # CUDA 11.8

Important: Both CuPy and PyTorch-CUDA are required runtime dependencies but cannot be auto-installed by pip because the correct package varies by CUDA version. Install them before installing PySIFT.

Install PySIFT

# From GitHub
pip install git+https://github.com/SivaIITM/PySIFT.git

# Or from source
git clone https://github.com/SivaIITM/PySIFT.git
cd PySIFT
pip install -e .

Full install (all dependencies at once)

pip install cupy-cuda12x   # or cupy-cuda11x
pip install -r requirements.txt
pip install git+https://github.com/SivaIITM/PySIFT.git

Recommended: depth-aware stitching

PySIFT uses MiDaS monocular depth estimation to split inliers into depth bands, giving each band its own homography. This significantly improves stitching quality for scenes with foreground/background parallax. Without timm, stitching falls back to a single global homography.

pip install timm>=0.9

Optional dependencies

# Learned descriptors (HardNet, HyNet, OriNet)
pip install kornia

# YAML config file support
pip install pyyaml

# Or install all optional deps at once
pip install -e ".[all]"

Quick Start

Python API

from pysift import PySIFT, GPUPyStitch

# Feature extraction
sift = PySIFT()
keypoints, descriptors = sift.detectAndCompute(gray_image)

# Panoramic stitching (2 or 3 images)
stitcher = GPUPyStitch()
panorama = stitcher.stitch(img_left, img_right)

CLI

# Basic stitching
pysift-stitch left.jpg right.jpg

# 3-image panorama with output directory
pysift-stitch left.jpg center.jpg right.jpg -o results/

# With config file
pysift-stitch left.jpg right.jpg --config config.yaml

# Learned pipeline
pysift-stitch left.jpg right.jpg --descriptor hardnet --matcher lightglue

Configuration Presets

Preset	Orientation	Descriptor	Matcher	Use Case
Classic	histogram	sift	ratio	Fastest. Full Lowe 2004 pipeline
Modern	histogram	sift	lightglue	Best accuracy with proven detection
Learned	orinet	hardnet	lightglue	Fully modern pipeline
Mobile	histogram	sift	ratio	Large phone images (auto-resize + denoise)

See config.yaml for all parameters and presets.

Requirements

Hardware

• NVIDIA GPU with CUDA support (tested on RTX 3050 4GB and above)
• CUDA Toolkit 11.x or 12.x

Software

Package	Version	Purpose
Python	>= 3.9	Runtime
PyTorch	>= 2.0	Tensor ops, SVD, CUDA graphs
CuPy	>= 12.0	GPU arrays, CUDA kernels
Numba	>= 0.57	JIT-compiled CUDA kernels
NumPy	>= 1.22	CPU array operations
OpenCV	>= 4.5	Image I/O, CLAHE
kornia	>= 0.7	Optional: HardNet, HyNet, OriNet
timm	>= 0.9	Optional: MiDaS depth estimation
PyYAML	any	Optional: config file support

Citation

@article{sivakumar2026pysift,
  title   = {PySIFT: GPU-Resident Deterministic SIFT for Deep Learning Vision Pipelines},
  author  = {Sivakumar, K.S.},
  journal = {arXiv preprint},
  year    = {2026}
}

License

This project is licensed under the MIT License -- see the LICENSE file for details.