Supervision

Snorkel is a system for programmatically building and managing machine-learning training data through weak supervision. It encodes expert knowledge as labeling functions, uses those functions to generate labels at scale, and supports data labeling, augmentation, slicing, and management for adaptable training datasets. The project began at Stanford and is now complemented by Snorkel Flow, an end-to-end AI application development platform based on the same ideas.

HFlow is an open-source SDK from Hebbian Robotics for multimodal data pipelines in robotics and physical AI. It lets teams implement transformations, quality checks, labels, and enrichments as plain Python functions while HFlow handles orchestration, provenance, storage, versioning, and dataset curation. The pipeline accepts supported MCAP episodes and can import LeRobot Dataset v3 repositories. It runs code in-process for development or generates Airflow 3 DAGs for scheduled execution, producing canonical MCAP episodes, artifacts, provenance records, and a Parquet catalog. DuckDB SQL creates version-pinned manifests for curation, while a queryable catalog records metadata and quality evidence without requiring the underlying recordings to be loaded.

ABot-Recon is a streaming 3D-reconstruction model and inference tool for reconstructing long video streams from video frames alone. At each step, it caches key-value features from the preceding 11 frames, predicts a point map in the current camera coordinate system, estimates the adjacent relative camera pose, and composes those poses sequentially into a global trajectory and point cloud without persistent learned long-range memory. It can output camera poses, relative poses, local and world point maps, colors, confidence maps, and run metadata. An optional loop-closure backend retrieves candidate revisited-frame pairs using DINOv2-SALAD descriptors, predicts relative-pose constraints with ABot-Recon, and refines the trajectory through sparse pose-graph optimization. The repository provides Python and command-line interfaces, a public model checkpoint, a demo, evaluation code, and visualization utilities. The released configuration targets Linux, Python 3.10 or later, PyTorch 2.5.1, and CUDA 12.1; source code is licensed under Apache License 2.0, while the model weights have separate terms in MODEL_LICENSE.md.

LingBot-Map is a feed-forward 3D foundation model from the Robbyant Team for streaming 3D reconstruction from image sequences and video. Its Geometric Context Transformer unifies coordinate grounding, dense geometric cues, and long-range drift correction through anchor context, a pose-reference window, and trajectory memory. The system uses paged KV-cache attention for streaming inference and supports interactive browser-based reconstruction with point-cloud visualization, keyframe intervals, windowed processing for long sequences, and sky masking. It also provides an offline rendering pipeline for image folders or video, along with evaluation pipelines for datasets including KITTI and Oxford Spires. The repository reports approximately 20 FPS inference at 518×378 resolution on sequences exceeding 10,000 frames, and documents PyTorch installation with FlashInfer as the recommended attention backend and native SDPA as a fallback.

VidMap is an offline structure-from-motion system for video, developed by the Computer Vision and Geometry group (cvg). It combines temporal feature tracks, loop closures, metric depth, and global optimization to estimate camera poses, camera intrinsics, and a sparse 3D map from video or extracted frames. The Python package writes the reconstruction as a COLMAP model and supports optional browser-based HTML visualization and Rerun playback or flythrough recordings. The repository provides setup and execution workflows built around COLMAP, PyCOLMAP, PyTorch, xFormers, and GPU acceleration through CUDA and cuDSS.