3D SEGMENTATION

3D LABELS FOR PERCEPTION TEAMS

We turn your sensor data into high quality 3D segmentation labels for mobility and robotics, including semantic, instance, and panoptic segmentation.

TYPES OF SEGMENTATION

iMerit tailors image segmentation services to your project goals, balancing accuracy and throughput. Together, we define requirements and deploy custom workflows that scale to your image analysis demands.

Semantic Segmentation

3D SEMANTIC SEGMENTATION

Semantic segmentation helps to train computer vision models that involve taking as input as some raw data like 2D images and assigning a label to every pixel in the image.
Instance Segmentation

3D INSTANCE SEGMENTATION

Instance segmentation helps to train the machine learning model at the instance level where multiple objects of the same class are assigned as one class but as separate instances.
Panoptic segmentation

3D PANOPTIC SEGMENTATION

Coupling instance and semantic segmentation, panoptic segmentation performs pattern recognition and helps to identify the pixels in images as belonging to a class and to identify what instances of that class they belong to.

Common Use Cases

AUTONOMOUS VEHICLES

Dense scene understanding for road structure, boundaries, and obstacle context across complex driving scenarios.

ROBOTICS

Traversability and semantic mapping signals for navigation in dynamic indoor and mixed environments.

MAPPING AND LOCALIZATION

Structure and boundary labels to support lane-level understanding, map alignment, and localization references used in mapping and navigation stacks.

INDUSTRIAL MOBILITY

Segmentation for yards, depots, ports, and large operational sites where surface context and zones matter as much as object detection.

AERIAL MOBILITY AND DRONES

Segmentation for terrain mapping, obstacle awareness, and infrastructure inspection across complex outdoor environments.

OIL, GAS AND MINING

3D segmentation for unstructured, high-variance environments such as open pits, refineries, and remote sites. Supports terrain and surface understanding, obstacle awareness, and safer autonomy around heavy equipment and infrastructure.

3D Segmentation for LiDAR Annotation

KEY FEATURES

3D Segmentation Key features
  • Multi Tool Annotation: Cuboids for fast 3D bounding boxes, polygons for irregular shapes, and brush tools for fine-grained segmentation when precision matters.
  • Flexible Workflows: Switch annotation methods seamlessly based on object complexity, edge cases, and acceptance criteria.
  • Fast Labeling Cycles: Combine high-throughput cuboids for bulk labeling with precision tools for complex geometry to reduce rework and QA loops.
  • Native 3D Point Cloud Support: Annotate directly in LiDAR point clouds to preserve depth and spatial relationships, with optional projection workflows when needed.
  • Broad Class Coverage: Supports vehicles, pedestrians, cyclists, road infrastructure, vegetation, buildings, and custom taxonomies.
“iMerit’s incredibly knowledgeable data labelers are part of our team and are the right people for our enrichment efforts.”
– Director of Data Products, Research Institute of Autonomous Driving

QUALITY BUILT FOR SEGMENTATION

BOUNDARY ACCURACY
Clear edge policies and targeted review for high-impact boundaries relevant to planning and mapping.

TAXONOMY CONSISTENCY

Stable class definitions and drift prevention across batches to maintain training reliability at scale.

TEMPORAL CONSISTENCY
Sequence checks to reduce label flicker and improve training signal in time-ordered data.

OPERATIONAL QA
Acceptance criteria, sampling plans, and structured review loops aligned to your internal validation process.

Case Studies

ACCELERATING 3DSENSOR FUSION

iMerit collaborated with a leading autonomous vehicle company to deliver high-precision LiDAR and 3D point cloud annotations required for a robust 3D perception stack. We deployed a specialized team of experts who completed a rigorous three-level training program and utilized the client’s proprietary tools to meticulously annotate complex road features and targets. This partnership provided the accurate “ground truth” data necessary for the client to ensure reliable object detection and safe autonomous movement of their vehicles

LIDAR ANNOTATIONFOR LEADING AV COMPANY

We partnered with this company to support them with data annotation across 2D images and 3D point clouds. 3D perception systems are highly dependent on data quality for improved performance, and the company was looking at target identification in LiDAR frames with lane marking, road boundaries, traffic lights, and others.

With our human-in-the-loop workflows, data labeling on 3D LiDAR frames for poles, pedestrians, signs, cars, and barriers, was achieved seamlessly and accurately.

How We Deliver at Scale

Rapid Scalability

Scale 3D segmentation programs quickly with a trained global workforce and established QA workflows. We support pilot-to-production ramp-ups without sacrificing label consistency across large, multi-geo datasets.

All-sensors Support

iMerit’s team has experience with 3D perception systems with all different types of sensors, including LiDAR and RADAR, while supporting multiple camera types.

High-quality

3D segmentation quality depends on boundary accuracy, class consistency, and stability across sequences. Our workflows include structured reviews and in-process validation to reduce drift, improve consistency, and keep training signal clean.

Tool Ecosystem

In case of specific requirements, we train our teams to work on the client’s proprietary tools. Alternatively, we have in-house annotation solutions or can work on any other 3rd party annotation tools.

Custom Engineering

iMerit has an engineering team that develops custom tools and plugins for our customers. Some features of custom engineering are version control to customer endpoints, streamlined automatic uploads, and custom API integration.

Security

ISO 27001:2013 certified, SOC 2 compliant, GDPR certified, AICPA SOC certified, and HIPAA compliant. We create instances of our internal tools in specific regions and countries to ensure data security.

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Video

3D PCT DEMO VIDEO

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Frequently Asked Questions

Multi-sensor fusion combines inputs from sensors such as cameras, LiDAR, and radar to improve perception reliability. It helps models handle occlusion, lighting changes, and sensor-specific blind spots by learning from complementary signals.

Multi-sensor fusion programs typically include 2D bounding boxes, 3D bounding boxes (cuboids), polygon annotation, semantic segmentation, and object tracking. The right mix depends on whether the perception stack prioritizes detection, tracking, mapping, or dense scene understanding.

We apply a unified labeling taxonomy and label specification that is defined for your program, then enforce it consistently across sensor streams. Cross-stream QA checks reduce mismatch between 2D and 3D labels and help maintain class and boundary consistency across sequences.

Yes. We support video and multi-frame sequences, including object tracking and sequence consistency checks to reduce label drift across frames. This is important for motion understanding and temporal fusion use cases.

Ango Hub supports multi-sensor fusion workflows by enabling teams to manage annotation across synchronized sensor streams and apply consistent labeling rules within a governed workflow. It helps streamline production with role-based access controls, built-in review and QA steps, and exports aligned to your training and evaluation pipeline.

GETTING

STARTED!

The need for generative AI training data services has never been greater. iMerit combines the best of predictive and automated technology with world-class subject matter expertise to deliver the data you need to get to production, fast.