Pixeltovoxelprojector

Projects motion of pixels to a voxel

Overview

PixelToVoxelProjector reconstructs 3D motion or object trajectories from multi-camera image sequences using voxel-based ray accumulation. It provides both C++ and Python tools for processing images, detecting motion, casting rays into a 3D voxel grid, and visualizing results.

Features

1. Multi-camera motion detection: Detects moving pixels between consecutive frames for each camera.
2. Ray casting: Projects rays from camera pixels into a shared 3D voxel grid using voxel DDA.
3. Voxel accumulation: Aggregates brightness/motion evidence in a 3D grid.
4. Flexible metadata: Camera parameters and image info are loaded from a JSON file.
5. Python visualization: Visualizes the reconstructed 3D scene, camera positions, and motion using matplotlib and pyvista.
6. Astro support: Includes tools for processing astronomical FITS images and mapping sky coordinates.

Requirements

1. C++17 compiler
2. Python 3.8+

Example Workflow

1. Prepare metadata.json and images in a folder.
2. Run the C++ pipeline to generate voxel_grid.bin.
3. Use Python scripts to visualize and analyze the voxel grid.

File Structure and metadata format

See docs.

Build Instructions

C++ Executable

make

This builds ray_voxel.

Python Extension (process_image_cpp)

# Optionally create a virtual env (recommended)
python3 -m venv .venv
# or with uv:
# uv venv
# Then activate with (linux)
source .venv/bin/activate
# Windows
# .venv/bin/activate.bat or .venv/bin/Activate.ps1

# Install the dependencies
pip install pybind11 setuptools
python setup.py build_ext --inplace

This builds the process_image_cpp Python module.

Usage

C++ Pipeline

./build/ray_voxel metadata.json images_folder output_voxel_grid.bin

• Processes images and metadata, outputs a binary voxel grid.

Python Visualization

python spacevoxelviewer.py

• Processes FITS images, accumulates into a voxel grid, and visualizes results.

python voxelmotionviewer.py

• Loads and interactively visualizes a voxel grid.