FiftyOne Point Cloud Support

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Point Cloud Pain Points

Working with point cloud data is challenging

Modern ML teams working with 3D and sensor data—such as point clouds from LiDAR—struggle with fragmented workflows, clunky tooling, and poor dataset understanding.

Point Cloud Support

FiftyOne provides a unified interface for analyzing point clouds

Visualize point cloud data at scale

Seamlessly inspect point clouds, LiDAR annotations, and multimodal sensor data in a powerful, interactive interface.

Debug and explore model outputs

Identify failure modes, compare predictions vs. ground truth, and drill into edge cases across complex 3D scenes.

Compare datasets and experiments

Spot differences across versions, identify covariate shift, and track performance on curated subsets—all in one place.

Point Cloud Features

How FiftyOne helps you manage point cloud data

Effortlessly manage millions of point cloud samples

FiftyOne makes it easy to organize large-scale point cloud data alongside images, video, and metadata—essential for autonomous vehicles and sensor-driven systems.

Point cloud-first: Manage LiDAR, radar, and depth-based point cloud data at scale

Multimodal support: Combine point clouds with images, videos, frames, geolocation, and more

Custom metadata: Track time of day, sensor IDs, location, weather, and other context critical to your AI models

Compatible with any model: Object detection, lane detection, semantic segmentation, and more

Quickly find the subsets of data you want

Sifting through massive amounts of data is like searching for a needle in a haystack. Pinpoint samples of interest in seconds using FiftyOne.

Create meaningful, balanced datasets: query samples by metadata to correct for imbalances

Accelerate training data selection: quickly find unique scenarios and anomalies in your data streams

Cover the edge cases: identify hard samples to strengthen your datasets and model performance

Improve model performance with confidence

Models can struggle on new data. FiftyOne helps you visualize, compare, and debug performance—so you can deploy with confidence.

Find failure modes: Explore point cloud predictions at the sample level
Continuously evaluate: Track model and dataset quality across training cycles
Version datasets: Manage and roll back dataset changes with ease

Find failure modes: Explore model predictions and debug at the sample level

Embrace active learning: integrate FiftyOne into your training pipeline to improve model performance and datasets with every update

Version datasets: Manage and roll back dataset changes with ease

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