LightGlue is a PyTorch library for local feature matching between image pairs. It implements a deep neural network that takes sparse keypoints and descriptors (extracted by models like SuperPoint, DISK, ALIKED, or SIFT) and outputs correspondences. The architecture uses stacked self- and cross-attention transformer layers with adaptive pruning (width and depth) for speed, achieving state-of-the-art accuracy at high throughput. The repository provides pretrained weights, a benchmark script, and a demo notebook. It is designed for inference only; training code is in a separate repository (glue-factory). The code is well-structured, with clean separation between feature extractors, the matcher, and utility functions. It supports FlashAttention and PyTorch compilation for performance.

NoPoSplat is a research application for novel view synthesis and camera pose estimation from unposed image pairs. It predicts 3D Gaussian parameters (means, covariances, harmonics, opacities) in canonical space using a vision transformer backbone (CroCo, ResNet, or DINO) with a DPT head, then renders images via differentiable Gaussian splatting. The system eliminates the need for known camera poses during training/inference by estimating poses implicitly through geometric consistency losses. Built on PyTorch Lightning with Hydra configuration, it supports multi-GPU training and evaluation on datasets like RealEstate10K, ACID, DTU, and ScanNet++. The codebase emphasizes modularity through component registries, composable loss functions, and chainable data preprocessing shims.