The Right Solution for Every Sensor-Data-Capture Challenge
Single-sensor approaches leave your AI vulnerable to real-world conditions. Our sensor fusion data annotation services create the robust training data your models need to perform reliably across diverse environments and use cases.
We solve the prominent data capture challenges that sensors like LiDAR, cameras, Radars, GPS, or ultrasonic radars face (lighting, weather, or environmental interference), especially in fields like autonomous vehicles and robotics, where a comprehensive understanding of the surrounding environment is critical to AI performance. Our team handles data synchronization, sensor interactions, and 3D spatial relationships to create specialized training datasets.
Individual sensors have blind spots that can compromise AI performance when it matters most:
We combine data from all your sensor modalities into precisely labeled training datasets that give your AI systems three critical advantages:
Our team works with a broad range of industry-standard and proprietary platforms to deliver precise, production-ready training datasets. Whichever data labeling tools you prefer, this flexibility ensures that we can adapt seamlessly without disrupting your workflows, compromising accuracy, or delaying project timelines.
From autonomous vehicles navigating city streets to precision farming drones scanning vast fields, today's AI systems must interpret complex, ever-changing environments with exceptional accuracy. With multi-sensor annotation workflows, we power advanced AI applications across industries, delivering robust datasets required for safe, efficient, and scalable deployment.
Our advanced multi-modal data labeling capabilities address the full spectrum of spatial, temporal, and semantic requirements in multi-sensor datasets. From labeling objects in 3D space to handling different sensor types, like LiDAR, radar, and cameras, and their corresponding data formats, our services are purpose-built to enhance data reliability, strengthen model training pipelines, and ensure deployment-ready accuracy for complex, real-world AI applications.
2D–3D Linking & Sensor Synchronization
We align 2D camera imagery with corresponding 3D point cloud annotation data and synchronize data streams from LiDAR, radar, and other sensors. This ensures that every object is consistently identified across all modalities and timeframes, forming the foundation for accurate sensor fusion data labeling.
3D Cuboid Annotation
We create precise three-dimensional bounding boxes around objects like vehicles, pedestrians, and infrastructure elements to capture exact position, size, and orientation. This spatial data is vital for training AI perception models to recognize and interact with real-world objects.
Specialized LiDAR Annotation
Our team handles complex 3D LiDAR point cloud annotation for laser-based distance measurement data, producing precise object labels from dense point clouds. This helps power perception systems in autonomous vehicles, drones, and robotics.
Point Cloud Semantic Segmentation
We classify every point in a 3D dataset into specific categories, such as roads, vehicles, buildings, vegetation, and other scene elements. AI models trained on this granular, point-level labeling can achieve precise object detection and boundary definition and seamlessly integrate this spatial understanding into multi-sensor perception models like autonomous navigation, robotics, and advanced mapping.
Panoptic Segmentation
Our sensor fusion annotation service combines semantic segmentation (classifying each pixel or point by object type) with instance segmentation (identifying each individual object) to deliver complete scene understanding. This dual approach is critical for object tracking, movement prediction, and situational awareness in transportation, security, and industrial automation environments.
Polygon & Polyline Annotation
With precise polygons and polylines, we capture complex object boundaries and linear features, such as lane markings, road edges, and building outlines. In the context of sensor fusion data annotation, these techniques define irregular shapes and continuous paths with exact spatial alignment, enabling AI models to interpret environments accurately for navigation, mapping, and geospatial analysis.
Multi-Frame Tracking
We track and maintain consistent object identities across sequential frames, capturing movement patterns, speed, and trajectory changes over time. This temporal annotation enables AI systems to understand object behavior and predict future movements, supporting custom 3D sensor fusion dataset creation for autonomous vehicle behavior modeling, drone flight path planning, and real-time monitoring systems.
Bird's-Eye-View (BEV) Labeling
We transform complex 3D data into simplified 2D top-down perspectives, making spatial layouts easy to interpret. This bird's-eye view labeling is ideal for identifying drivable areas, designing navigation paths, managing traffic flow, and enabling safe route planning in autonomous vehicles, robotics, and smart city infrastructure.
Human-in-the-Loop Quality Assurance
We integrate human expertise into the quality assurance stage of multi-sensor fusion data annotation, ensuring that AI-generated labels are validated and corrected by trained specialists. This process catches edge cases, resolves ambiguities, and upholds annotation consistency across LiDAR, camera, radar, and depth sensor datasets.
Our multi-sensor 3D annotation services follow a structured, quality-driven process to turn raw LiDAR, camera, and radar inputs into accurate, application-ready training datasets. This workflow ensures your AI systems achieve maximum perception accuracy and perform reliably in diverse, real-world environments.
01
Data Preprocessing & Calibration
Raw sensor data is cleaned, aligned, and calibrated to maintain spatial accuracy across modalities.
02
Sensor Data Fusion
Synchronized sensor streams are merged into a unified 3D representation for comprehensive analysis.
03
Multi-modal Data Annotation
Objects are labeled with 3D cuboids, segmentation, and other techniques to capture accurate details.
04
Cross-Sensor Verification
Annotations are validated across sensor types to reduce false positives/negatives and improve detection accuracy.
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Human-in-the-Loop Quality Assurance
AI-generated labels are reviewed and corrected by experts to ensure consistent, deployment-ready outputs.
06
Final Dataset Delivery
Production-ready multi-sensor datasets are delivered in custom formats for seamless AI model training.
SunTec.ai brings field-tested expertise in high-precision data labeling, purpose-built for complex 3D sensor fusion workflows. Our global team delivers scalable, tightly synchronized annotations for multi-sensor datasets, applying domain-specific knowledge with human-in-the-loop QC to maintain accuracy across large-scale projects. Every engagement is backed by ISO 27001 and ISO 9001 certifications, CMMI Level 3 compliance, industry-specific standards like HIPAA, and an NDA-enforced workforce.
Calibration-Aware Labeling
We deliver perfectly aligned and synchronized datasets across LiDAR, camera, and radar feeds, reducing downstream model errors and rework.
Human-in-the-Loop Accuracy
Our workflows combine automated pre-labeling with expert human validation, ensuring both speed and uncompromising annotation quality with 99.95% accuracy.
Customizable Workflows
We design annotation pipelines around your specific sensors, formats, and output requirements, ensuring fit-for-purpose data delivery.
3D Annotation Expertise
Our annotators are trained in LiDAR interpretation, 3D point cloud labeling, and sensor fusion concepts, bringing proven domain knowledge to complex datasets.
Get a No-Obligation Multi-sensor Annotation Sample
Raw sensor data alone can't train reliable AI models because each modality has limitations — LiDAR struggles in rain or fog, cameras fail in low light, and radar lacks fine detail. 3D sensor fusion annotation services help you overcome these gaps by aligning and labeling multi-sensor inputs into a unified 3D dataset. This ensures your AI systems get complete spatial understanding, enabling accurate decision-making in real-world environments.
Yes. We can handle custom 3D sensor fusion dataset creation for highly specific use cases, like annotating underground mining vehicles with LiDAR + thermal cameras or drone sensor data fusion for agricultural crop inspection. Our annotation workflow adapts to your object classes, environmental variables, and required label formats, so your model learns exactly what it needs to perform in production.
In multi-sensor annotation, mismatched resolutions, coordinate systems, and frame rates can cause serious misalignment issues. We standardize all incoming LiDAR, radar, and camera feeds, handle their native data formats, and apply sensor fusion data labeling workflows that preserve both spatial and temporal integrity. This means your AI trains on data that actually matches the real-world conditions it will face.
Our sensor synchronization workflows correct timestamp offsets, align coordinate systems, and account for sensor latency. This ensures your annotated sensor fusion datasets have perfect temporal and spatial alignment, drastically reducing false detections.
Absolutely. We offer LiDAR annotation service with collaborative QA checkpoints. You can review 3D LiDAR point cloud annotation batches at defined intervals, suggest class or attribute changes, and approve before the next batch starts. This way, you retain full oversight while we handle the labor-intensive work.
Free Sample