MCAP Layers Explained

MCAP processing in Rerun uses a layered architecture where each layer represents a different way to interpret and extract data from the same MCAP source. By default, when opening a file Rerun analyzes an MCAP file to determine which layers are active to provide the most comprehensive view of your data, while avoiding duplication. You can specify which layers to use during conversion, allowing you to extract exactly the information you need for your analysis.

Understanding layers with an example understanding-layers-with-an-example

When multiple layers are enabled, they each process the same messages independently, creating different component types on identical entity paths. This can result in data duplication—for instance, enabling both raw and protobuf layers stores the same message as both structured field data and raw binary blobs.

Consider an MCAP file from a ROS2 robot containing sensor data on the topic /robot/camera/image_raw with ROS2 sensor_msgs/msg/Image messages:

• With only the ros2msg layer: Creates an Image archetype for direct visualization in Rerun's viewer
• With only the raw layer: Creates an McapMessage containing the original CDR-encoded message bytes
• With both layers enabled: All representations coexist on the same entity path /robot/camera/image_raw

Schema and statistics layers schema-and-statistics-layers

The schema layer extracts structural information about the MCAP file's organization, creating metadata entities that describe channel definitions, topic names with their message types, and schema definitions. This layer is particularly useful for understanding unfamiliar MCAP files or getting an overview of available topics and channels before deeper processing.

The stats layer computes file-level metrics and statistics, creating entities with message counts per channel, temporal ranges, file size information, and data rate analysis. This gives you insight into the scale and characteristics of your dataset for quality assessment and planning storage requirements.

Message interpretation layers message-interpretation-layers

Semantic interpretation semantic-interpretation

The ros2msg and foxglove layers provide semantic interpretation and visualization of standard ROS 2 and Foxglove message types, creating meaningful Rerun visualization archetypes from data. Unlike the protobuf layer, this layer understands the semantics of the messages and creates appropriate visualizations: images become Image, point clouds become Points3D, IMU messages become SeriesLines with the data plotted over time, and so on.

See Message Formats for the complete list of supported message types.

Protobuf decoding protobuf-decoding

The protobuf layer automatically decodes protobuf-encoded messages using reflection, creating structured component data based on the protobuf schema. Message fields become Rerun components that you can query and analyze.

However, this layer provides structured access without semantic visualization meaning. While the data becomes queryable, it won't automatically appear as meaningful visualizations like images or point clouds, it gives you the data structure, not the visual interpretation.

The raw layer the-raw-layer

The raw layer preserves the original message bytes without any interpretation, creating blob entities containing the unprocessed message data. Each message appears as a binary blob that can be accessed programmatically for custom analysis tools.

Recording info recording-info

The recording_info layer extracts metadata about the recording session and capture context, creating metadata entities with information about recording timestamps, source system details, and capture software versions.

Layer selection and performance layer-selection-and-performance

Selecting layers selecting-layers

By default, Rerun processes MCAP files with all layers active. You can control which layers are used when converting MCAP files via the CLI using the -l flag:

# Use only specific layers
rerun mcap convert input.mcap -l protobuf -l stats -o output.rrd

# Use multiple layers for different perspectives
rerun mcap convert input.mcap -l ros2msg -l raw -l recording_info -o output.rrd

Accessing layer data accessing-layer-data

Each layer creates different types of components on entity paths (derived from MCAP channel topics) that can be accessed through Rerun's SDK:

• Data from the ros2msg layer and supported Foxglove messages appears as native Rerun visualization archetypes (see here for an overview)
• Other data from the protobuf or ros2_reflection layers appears as structured components that can be queried by field name or manually added to certain views (example)
• Data from the raw layer appears as blob components containing the original message bytes
• Metadata from schema, stats, and recording_info layers appears as dedicated metadata entities

For more information on querying data and working with archetypes, see the Data Queries documentation.

Each of these layers contributes their own chunks to the Rerun-native data. Below is a table showing the mapping between MCAP data and Rerun components: