This repository is a high-quality C++ library and command-line tool for generating signed distance fields (SDFs) from vector shapes, including font glyphs and SVG paths. It supports conventional single-channel SDFs, perpendicular distance fields, multi-channel SDFs (MSDF) for sharp corner preservation, and combined MTSDF with an additional true distance channel. The architecture is modular: a dependency-free core library implements all key data structures (Shape, Contour, edges, bitmap, distance transformation) and algorithms (distance computation, edge coloring, error correction). An extensions layer provides font loading via FreeType, SVG parsing via TinyXML2 or dropXML, PNG output via libpng or LodePNG, and optional geometry preprocessing via Skia. The standalone executable wraps all functionality into a comprehensive CLI with many configuration options. The code is well-structured, performant (supports OpenMP parallelization), and actively maintained with regular releases and a detailed changelog. While the core is purely numerical, the extensions introduce external dependencies, but the project provides build presets and vcpkg integration for easy compilation.
PicoGK is a compact, open-source computational geometry kernel developed by LEAP 71 for engineering design and additive manufacturing. It provides a C# API over a high-performance native voxel engine (OpenVDB). The core abstraction is the Signed Distance Field (Voxels), supporting boolean operations, offsets, shells, lattice generation, implicit modeling, and meshing. It includes a robust 3D viewer, industry-standard IO (STL, VDB, CLI), and utilities for slicing and cross-section visualization. While primarily a library, its numerical core and debugging utilities carry a strong data-processing character.
GS-SDF is a research prototype from HKU-MARS that fuses LiDAR-augmented 3D Gaussian Splatting with a neural Signed Distance Field (SDF) for photorealistic rendering and high-fidelity surface reconstruction. The system ingests data from multiple sensor modalities (LiDAR, RGB-D, stereo) via a modular data parser hierarchy supporting Replica, FAST-LIVO2, KITTI, COLMAP, Oxford Spires, and other formats. It trains a neural SDF field with eikonal and curvature regularization, then initializes and refines 3D Gaussians guided by the SDF to produce view-dependent color and geometry. The pipeline includes real-time ROS visualization (RVIZ), offline mesh extraction via a custom CUDA marching cubes implementation, and evaluation scripts for image metrics (PSNR, SSIM, LPIPS) and structural metrics (Chamfer distance, F-score). The codebase is pre-release, with many TODOs and commented-out code, but exhibits strong modularity in its data loaders, loss functions, optimizer utilities, and coordinate transformations. While primarily a research codebase, it includes application-like features such as a ROS node with interactive keyboard commands and headless rendering modes.