rerun-io/rerun

Features

• Training Data Pipelines - Streams logged recordings directly into PyTorch or GPU buffers to eliminate manual data export steps.
• 3D Scene Renderers - Provides a high-performance 3D renderer for point clouds, meshes, and camera poses to analyze physical environments.
• Robotics Visualization Tools - Visualizes synchronized sensor streams, 3D point clouds, and coordinate transforms to analyze physical robotic agents.
• Dataset Loaders - Feeds logged recordings directly into PyTorch and GPU training pipelines as efficient dataset loaders.
• Multimodal AI Systems - Captures and stores synchronized images, point clouds, and time series for debugging embodied AI systems.
• Spatial Pose Management - Defines coordinate frames and spatial poses to position entities accurately in 3D space.
• Stream Synchronization Utilities - Assigns deterministic timestamps across concurrent data streams to ensure synchronized playback of multimodal sensor data.
• Robotics and Embodied AI - Provides tools for logging and reviewing multimodal sensor data to identify failures in embodied AI models.
• File Format Processing - Parses channels, schemas, and statistics from MCAP recording files for analysis.
• Columnar Storage Engines - Uses a column-oriented storage format for multimodal time-series data to enable efficient querying and random access.
• Temporal Stream Synchronization - Renders synchronized streams of images and point clouds to analyze multimodal sensor data over a shared timeline.
• Time-Series Visualizers - Provides interactive scalar line series and bar charts to track numerical metrics over time.
• Multimodal Sequence Visualizers - Renders multimodal data in sync to scrub through episodes and compare sensors over time.
• ML Dataset Lazy-Loading - Streams recordings directly into machine learning frameworks via on-the-fly decompression and random seeking.
• Local Data Persistence - Saves high-frequency sensor logs and AI outputs to binary files for offline playback and sharing.
• MCAP File Integration - Loads timestamped messages from MCAP containers via file import or SDK integration for visualization.
• MCAP File Viewers - Provides a dedicated visualization interface for importing and parsing timestamped messages from MCAP recording files.
• Message Decoders - Decodes raw MCAP messages using reflection or archetypes to transform them into queryable components.
• Real-Time Data Streaming - Feeds real-time sensor data from webcams and depth sensors directly into the viewer for instant monitoring.
• Robotics Data Loggers - Captures and persists high-frequency sensor data and AI outputs into a queryable columnar format.
• Temporal Data Management - Assigns timestamps and durations to logged data to ensure deterministic playback and animation.
• 2D Spatial Rendering - Renders 2D spatial primitives including points, lines, boxes, and arrows within a coordinate system.
• Robotics-Specific - Renders domain-specific robotics data like occupancy grids and state changes on a synchronized timeline.
• Image Data Visualizers - Visualizes multi-dimensional tensors and various image types, including depth and segmentation maps.
• Multimodal Visualizers - Renders synchronized streams of 3D spatial data, images, and time-series metrics for AI and robotics analysis.
• Video Streaming - Manages and displays raw video streams and individual frame references synchronized with other sensor data.
• Geospatial Visualizations - Visualizes geographic data using latitude and longitude for mapping robot trajectories and locations.
• Sensor Data Visualizers - Visualizes high-frequency sensor telemetry and AI outputs to monitor the state of physical agents.
• Real-Time Monitoring Systems - Streams live telemetry and high-frequency sensor data to a remote viewer for real-time system inspection.
• Timeline Synchronization - Assigns time values to logged data across different threads to ensure temporal alignment during playback.
• Coordinate Transform Hierarchies - Manages spatial entities by logging a tree of relative poses to position 3D objects in a global coordinate system.
• Data Loaders - Integrates logged datasets into ML workflows using dedicated loaders to feed data into models.
• Dataset Integration - Exposes recording files as iterable datasets with random seeks and on-the-fly decompression for PyTorch frameworks.
• Multimodal Dataset Catalogs - Provides a searchable catalog and cloud storage for organizing large-scale multimodal recordings for team collaboration.
• Data Integration And Import - Allows importing and visualizing recorded data from external formats like images, video, and point clouds via plugins.
• Data Ingestion - Imports large datasets using record batches to move structured columnar data efficiently into the system.
• GPU-Accelerated Data Streams - Feeds column-aware and video-codec-aware data from recordings directly to GPUs to eliminate intermediate export steps.
• Data Query Management - Includes tools for retrieving and transforming multimodal data from a catalog for local analysis and team management.
• Data Sinking - Provides mechanisms for directing logged sensor data to various external destinations including gRPC servers and binary files.
• High-Volume Data Ingestion - Provides high-volume data ingestion capabilities to record multimodal sensor data from multiple threads and processes simultaneously.
• gRPC Data Streaming - Transmits logged data from producers to remote viewers in real-time using a high-performance gRPC framework.
• Robotic Dataset Catalogs - Organizes robotic data using a scalable catalog with version control and cloud storage for team access.
• Schema Mapping Engines - Decodes raw binary messages into queryable components using predefined data models and reflection for robotics types.
• Search and Indexing - Provides searchable indexes to locate specific byte ranges within large-scale recordings for efficient retrieval.
• Byte-Range Indexing - Catalogs large-scale datasets in object storage using index offsets to fetch specific data segments without full downloads.
• SQL Query Interfaces - Provides a SQL interface to query recorded data directly without requiring manual data exports.
• Structured Data Extraction - Extracts information from recorded data streams into structured dataframes for detailed analysis.
• Time-Series SQL Querying - Executes SQL queries over recording files to retrieve specific data columns and time ranges.
• Object Storage Indexing - Catalogs petabyte-scale multimodal data in object storage using byte-range reads for efficient querying.
• Spatial Data Analysis Tools - Renders 2D and 3D geometric primitives and geospatial coordinates on a shared temporal timeline.
• Remote Monitoring Transmission - Sends logged sensor data to remote viewers in real-time using high-performance network protocols.
• Remote Access Connectivity - Establishes network connections to remote viewer instances for real-time data streaming and visualization.
• Application Embedding Interfaces - Hosts the visualization interface directly within a host application process by embedding the renderer into a native window.
• Robotic Data Processors - Loads and transforms robotic sensor data streams using a declarative language to normalize nested structures.
• Annotation Collaboration - Allows teams to explore and annotate datasets within a shared viewing environment to identify and trace failures.
• Dataset Sharing - Provides a unified viewer for teams to collectively explore and annotate shared recording datasets.
• Catalog Management - Organizes multimodal logs into queryable segments using a dedicated catalog server.
• Embedded Interfaces - Integrates the visualization interface into notebooks or web pages for shared data exploration.
• Window Embedding - Hosts the visualization interface directly inside a native application window or process.
• Visualization and Analysis - SDK for logging and visualizing multimodal spatial data.
• Data Visualization and Processing - Time-aware visualization stack for multimodal sensor data.
• Robotics Libraries - SDK for logging and visualizing robotics data over time.

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