Visualization First
Before annotating, AV teams need to explore and understand their data. Avala’s multi-sensor viewer handles the full AV sensor stack:MCAP Playback
Upload MCAP recordings from your vehicle fleet and play back all sensor streams in a synchronized multi-panel viewer with 8 panel types.
Surround Camera + LiDAR
View all surround cameras alongside LiDAR point clouds with automatic calibration-aware projection for cross-sensor verification.
GPU-Accelerated 3D
Render LiDAR point clouds with WebGPU acceleration and 6 visualization modes: Neutral, Intensity, Rainbow, Label, Panoptic, and Image Projection.
Timeline Navigation
Scrub through drive logs, step frame-by-frame, and jump to specific timestamps. All panels stay synchronized across different sensor rates.
Data Types
Common Tasks
3D Object Detection
Label vehicles, pedestrians, cyclists, and static objects with 3D cuboids in LiDAR point clouds. The 3D annotation editor provides bird’s-eye, perspective, and side views for precise cuboid placement. Cuboids include full position (x, y, z), dimensions (length, width, height), and heading (yaw) parameters.Multi-Camera Projection
Annotate 3D cuboids in the LiDAR view and automatically project them onto surround camera images for visual verification. The viewer supports both pinhole and double-sphere camera models, so projection works with standard and fisheye lenses.Lane and Road Boundary Annotation
Use polyline tools to trace lane markings, curbs, and road edges in camera views. Polylines support connected segments with vertex-level editing, making them suitable for curved lanes and complex intersections.Temporal Object Tracking
Track objects across frames with consistent IDs for motion prediction and trajectory forecasting models. Object IDs persist across the sequence timeline, and the viewer’s frame-by-frame navigation makes it straightforward to verify tracking continuity.Scene Classification
Classify driving conditions at the scene level — weather (clear, rainy, foggy), time of day (daytime, dusk, nighttime), road type (highway, urban, rural), and traffic density. Classification labels apply to the entire frame and can be combined with object-level annotations.Avala Features Used
Example Pipeline
Getting Started
1
Upload your drive data
Create a dataset with
mcap data type and upload MCAP recordings from your fleet. For large datasets, use cloud storage integration to connect your S3 bucket directly.2
Explore in the viewer
Open a recording in the multi-sensor viewer. Verify that camera, LiDAR, and transform data are present. Check calibration by enabling LiDAR-to-camera projection.
3
Set up your annotation project
Create a project with 3D cuboid annotation, define your label taxonomy (vehicle, pedestrian, cyclist, etc.), and configure quality control settings.
4
Annotate and review
Your team annotates 3D cuboids with tracking IDs. Reviewers verify annotations using multi-camera projection to catch depth and heading errors.
5
Export and train
Export labeled data in your preferred format. Use the Python or TypeScript SDK to integrate exports into your training pipeline.
Next Steps
MCAP & ROS
Detailed guide for preparing and uploading multi-sensor recordings.
3D Cuboid Tool
How to place, adjust, and track 3D cuboids in the point cloud editor.
Recording Best Practices
Tips for recording data that visualizes and annotates well.
Quality Control
Set up multi-stage review workflows for production annotation.