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Avala supports five data modalities, each with GPU-accelerated visualization and purpose-built annotation workflows. This page covers what you can see, what you can label, and the supported formats for each type.

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Image

Visualization: Images are displayed in the image viewer with pan, zoom, and pixel-level inspection. When part of an MCAP recording, images appear in dedicated Image panels synchronized with other sensor streams. Annotation: Each image is annotated independently as a single frame. All seven 2D annotation tools are available: bounding boxes, polygons, segmentation masks, polylines, keypoints, classification, and object tracking (in video sequences). Supported formats: JPEG, PNG, WebP, BMP Use cases: Object detection, instance segmentation, semantic segmentation, image classification, keypoint detection, pose estimation.

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Video

Visualization: Videos are automatically converted to frame sequences on upload, enabling frame-by-frame playback with timeline scrubbing. Navigate forward and backward through frames, jump to specific timestamps, or play back at configurable speeds. Annotation: Annotators work frame-by-frame with object tracking across the timeline. Object IDs persist across frames for consistent identity assignment. All 2D annotation tools are available on each frame. Supported formats: MP4, MOV Use cases: Object tracking, action recognition, temporal event detection, driving scene labeling, behavior analysis.

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LiDAR / Point Cloud

Visualization: Point clouds are rendered in a 3D viewer with bird’s-eye view, perspective view, and side views. Six visualization modes let you color points by different properties: The viewer uses WebGPU compute shaders for frustum culling and level-of-detail rendering, keeping frame rates high even on dense scans. Annotation: Annotators place 3D cuboids with full position, dimension, and rotation control. Cuboid projections are displayed in synchronized camera views when camera calibration data is available. Supported formats: PCD, PLY Use cases: 3D object detection, autonomous driving perception, robotics navigation, scene reconstruction, HD map creation.

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MCAP

Visualization: MCAP recordings are displayed in the multi-sensor viewer with up to eight panel types: Image, 3D / Point Cloud, Plot, Raw Messages, Log, Map, Gauge, and State Transitions. Avala automatically detects message types in the recording and assigns topics to the appropriate panel type. All panels share a synchronized timeline for coordinated playback of camera, LiDAR, radar, IMU, and other sensor streams. The layout composer automatically builds an optimized panel arrangement based on the topics in your recording, or you can customize the layout manually. Navigate frame-by-frame, scrub to any timestamp, or play back the full recording with configurable speed. Annotation: Avala parses MCAP files to extract and synchronize sensor streams. Camera images are displayed alongside projected LiDAR data, enabling multi-camera annotation with 3D context. Annotators place 3D cuboids that project consistently across all camera views. Supported formats: MCAP (with ROS message support) Use cases: Multi-sensor fusion, surround-view perception, autonomous vehicle data labeling, robotics sensor calibration, fleet data review.

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Splat

Visualization: Gaussian Splat scenes are rendered in a WebGPU-accelerated 3D viewer. Navigate freely through photorealistic 3D scene reconstructions with smooth camera controls. The renderer uses GPU radix sorting, buffer pooling, and pipeline precompilation for real-time performance. Annotation: Annotators navigate the reconstructed environment and place 3D annotations directly in the scene. Classification labels can be applied to the full scene or individual regions. Supported formats: Gaussian Splat Use cases: 3D scene understanding, novel view synthesis annotation, spatial AI training data, environment mapping.

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Capabilities Comparison

The following table shows visualization and annotation capabilities for each data type:

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Upload Requirements

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Next Steps