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