Human-centered data.
Structured for what's next.
EgoData captures real-world human-perspective data and transforms it into structured, synchronized, and model-ready datasets for intelligent systems.
Real-world data is fragmented, unstructured, and hostile to intelligent models.
Modern physical-AI and robotics require massive amounts of high-fidelity, synchronized human-perspective data. Yet most real-world datasets are plagued by severe temporal drift, missing modalities, unstructured metadata, and spatial inconsistencies.
We build the foundational layer for physical AI.
EgoData provides the entire infrastructure required to capture, process, and validate human-perspective data at scale, delivering it in formats natively understood by modern machine learning architectures.
Deterministic Synchronization
Our hardware and software pipelines are built to ensure sub-millisecond sync across high-resolution video, spatial audio, IMUs, and external tracking systems.
Automated Structuring
Raw data is immediately mapped to rigorous, extensible schemas. Every frame, pose vector, and metadata tag is aligned and validated against predefined structural rules.
Comprehensive Modalities.
Intelligent systems cannot rely on vision alone. We capture a complete multidimensional representation of physical interaction.
RGB Video
High-fidelity egocentric video streams capturing full environmental context and human interactions.
Skeletal Pose
Real-time kinematic data mapping joint positions and limb orientations in 3D space.
Spatial Mapping
Continuous environmental scanning to place human actions within a persistent 3D geometry.
Motion Vectors
High-frequency acceleration and rotational velocity data from embedded sensors.
Temporal State
Absolute timestamping ensuring every modality is locked to a single deterministic timeline.
Semantic Metadata
Structured annotations defining objects, intent, gaze targets, and environmental state changes.
Interactive Dataset Viewer
A simplified look into how EgoData captures, synchronizes, and structures multiple streams of real-world data into a cohesive model-ready format.
Data Architecture
A deterministic pipeline transforming unstructured physical observations into machine-actionable knowledge.
Operational Domains
EgoData structured datasets are designed for the most demanding physical-AI and autonomous system applications.
Robotics & Automation
Providing ground-truth perspective data to train manipulation and navigation models on real-world sequences.
Physical AI
Enabling embodied agents to understand environments, physics, and human behavior natively.
Computer Vision
Enhancing egocentric object recognition, hand-object interaction tracking, and 3D scene understanding.
Human Behavior
Structuring sequence data for predicting intent, movement trajectories, and operational patterns.
Autonomous Systems
Training context-aware systems to operate safely alongside humans in unstructured environments.
Academic Research
Establishing benchmark datasets to evaluate sensor fidelity, field calibration, and multimodal architectures.
Technical Capabilities
Our data infrastructure is built for high-fidelity capture and sub-millisecond synchronization across multiple modalities.
// CAPTURE & SENSORS
- VIDEO RESOLUTION4K (3840x2160) per stream
- FRAME RATE60 FPS Native / 120 FPS Interpolated
- SPATIAL TRACKING6-DOF IMU + SLAM Integration
- AUDIOAmbisonic / Multi-channel Spatial
- ENVIRONMENTIndoor / Outdoor / Mobile
// SYNCHRONIZATION
- HARDWARE SYNCPTP (Precision Time Protocol)
- LATENCY< 1.0ms Max Drift
- FRAME ALIGNMENTGenlock / Timecode embedded
- SAMPLINGUniform Resampling Pipeline
- CALIBRATIONExtrinsic & Intrinsic Auto-calibrated
// STRUCTURING & ANNOTATION
- POSE ESTIMATION17-Keypoint Body / 21-Keypoint Hand
- SCENE UNDERSTANDINGSemantic Segmentation Masks
- METADATA EXTRACTIONGaze, Interactions, Environment
- QUALITY ASSURANCEAutomated Outlier Detection
- FORMATJSON / Parquet / HDF5
// MODEL-READY OUTPUTS
- TARGET SYSTEMSRobotics / VLM / Physical-AI
- DATASET STRUCTUREHierarchical Sequence Based
- COMPATIBILITYPyTorch / TensorFlow / JAX
- DELIVERYS3 Bucket / Direct API / Physical Media
- COMPRESSIONLossless H.265 / Zstd
Why EgoData?
Intelligence requires high-fidelity observation. We built EgoData because the bottleneck in modern AI and robotics is no longer compute or architecture—it's the absence of structured, synchronized, real-world physical data.
Unyielding Ground Truth
PRIN_01Synthetic data hallucinates physics. Third-person cameras lack human intent. We capture exactly what happens, from the perspective that matters, ensuring absolute ground-truth fidelity.
Deterministic Sync
PRIN_02Hardware-level PTP synchronization guarantees that every video frame, IMU vector, and spatial coordinate is locked to a single, drift-free timeline.
Schema Rigor
PRIN_03Raw data is useless without structure. Our pipeline enforces strict schemas, transforming unstructured observations into queryable, tensor-ready arrays.
Have a data challenge worth exploring?
Let's talk about the data you're working with, the questions you're trying to answer, and where EgoData may fit in your pipeline.