Services
Connecting the Dots in Point Cloud Data with Precision

LiDAR Annotation Services

Power your autonomous models and smart city models with highly accurate insights drawn from raw and complex point cloud data. We have deep expertise in validating labels, spotting and resolving issues, and labeling data so that it aids in the seamless functioning of your machine-learning models

LiDAR Annotation

Train Your ML Algorithms with Our Expert LiDAR Annotation Services

Proper labeling and classification of items in point cloud data is essential for three-dimensional element identification. It teaches the algorithm to recognize items and comprehend scenes during mapping and while riding driverless cars. However, there are significant obstacles in the way of obtaining and preserving this accuracy.

Most of these challenges are posed by its high density and complex 3D structure, which cause issues like surface reflectivity changes, overlaps, and occlusions. To top it, managing substantial amounts of LiDAR data comes with severe logistical and computational challenges.

At SBL, we understand the varied and deep intricacies of data labeling and solve them leveraging our professional LiDAR annotation services. With 18 years of experience in merging GIS and AI, we offer specialized expertise to tackle unique challenges associated with point data and occlusion.

Evolving Annotation: From Human-Driven to Self-Learning AI

Evolving Annotation: From Human-Driven to Self-Learning AI

Understand annotation and how it would contribute to fine tuning the self learning AI solutions.

Our seasoned experts have industry-specific knowledge to provide precise annotations suited to your application. We develop custom tools and invest in ongoing annotator training to label and classify your LiDAR data accurately and precisely.

Our LiDAR annotation capabilities

Our team has knowledge and proficiency in different types of LiDAR annotation techniques and can help you choose the one that best fits your project needs. Each of our techniques aims to ensure accurate and high-quality annotations to support machine learning models and enhance the performance of your application.

3D Cuboid & Bounding Box Annotation

3D Cuboid & Bounding Box Annotation

We bank on explicit 3D cuboid and bounding box annotations to capture the exact shape and orientation of objects for AI training. With this specific outlining technique, we assist in proper object detection as well as accurate depth perception for autonomous navigation systems.

3D Point Cloud Annotation

3D point cloud annotation entails labeling of objects within a 3D space to aid machine learning algorithms in comprehending the environment. We have expertise in 3D point cloud semantic segmentation, 3D point cloud image annotation and 3D point cloud continuous frame. This helps us classify every point in the LiDAR data accurately. As a result, you can feed your model with rich geometric information for easy understanding of the environment.

Semantic Segmentation

We deploy this technique to handle the complexities of LiDAR point cloud segmentation and accurately classify each point at the pixel level such as person, road, or building. In the process, we simplify the understanding of a scene structure and context to lend more precision autonomous navigation systems.
Landmark Annotation

Landmark Annotation

Our landmark annotation techniques help to identify features of specified locations with great accuracy. With our deep expertise, we mark key points in landmarks to create a training data set and assist your ML model gain a complete know-how of the landmark. Our detailed analysis of key landmark features has simplified landmark detection for ML models.
Instance Segmentation

Instance Segmentation

This technique is used to identify object classes and at the same time assign unique IDs to each object instance within an image. This ensures accurate tracking and precise understanding of multiple or overlapping objects in complex scenes, thus simplifying behavior analysis of separate objects. Our clients have availed our services to develop imaging, robotics, autonomous driving, etc.
Polyline Annotation

Polyline Annotation

With this annotation technique, we assist our clients to trace and outline polyline annotations around complex or irregular objects in images or videos. Our precise polyline labeling technique aids in avoiding white spaces and extra noise. We have expertise in providing this service to domains such as medical, agriculture, automotive, infrastructure inspection, industrial machinery, etc.

Tracking Annotation

We deploy this technique to connect the dots for a particular object across multiple LiDAR frames, In the process, we delineate the trajectories for tracked objects across consecutive frames. This aids in sharply tracking the dynamics and interactions of a particular object across various environments. Such in-depth behavior analysis and continuous trajectory prediction helps in increasing the precision of AI models.
HD Map Annotation

HD Map Annotation

This high-definition map annotation technique is put to use to annotate lanes, road structures, traffic signs, landmarks and other essential elements for streamlining the movement of self-driving vehicles. This detailed information helps in path planning, precise localization and enhancing safety and reliability of systems.

Proven Expertise, Best-in-Class Support

Diverse Experience
Domain experts from various industries

We have vast experience in annotating across various industries, from automotive to construction, allowing us to customize LiDAR annotation solutions for every specific need.

Proficient Team
Trained professional annotators

Our team comprises highly trained LiDAR annotation professionals. We efficiently allocate resources to meet your timelines and adapt to evolving annotation requirements.

ISO 27001 & privacy compliant
Secured facilities with complete supervision

We comply with ISO 27001 standards, and implement stringent security measures to secure client data and guarantee the highest level of confidentiality.

Accurate & Unbiased Annotation
Reduced bias in results

Our experts bring a variety of perspectives to the annotation process, minimizing the risk of bias in your training data. We back this with rigorous quality control measures to ensure the accuracy and reliability of annotation.

Click here to schedule a 30-minute consultation today

Discuss your LiDAR annotation needs with our experts and discover how we can transform your data into actionable insights.

Our Annotation & Labeling Workflow

At SBL, we follow a precise and collaborative approach to guarantee top-quality annotations for your machine learning projects. Our workflow consists of the following steps:

In the requirement gathering stage, we sit with your experts to comprehend the type of data annotation needed. Based on this understanding, our experts develop precise annotation guidelines along with the project’s scope and timeline.

In this stage, the data is cleaned and organized, to remove duplicates, inconsistencies and irrelevant information (sensitive data, like healthcare data, is anonymized). Next, the data is converted to compatible formats.

To aid our annotators, our experts create detailed guidelines consisting of a to-do list. It also includes clear instructions, examples, and visual aids to remove all confusion.

Our annotators are acquainted with the established guidelines and trained with the tools to be used. A pilot project is also conducted with a small subset of data to gather feedback and refine the guidelines. This helps our annotators develop a clear understanding of the project requirements.

Once the annotation process begins, the progress is monitored with continuous quality checks and reviews to ensure accuracy. The entire process is driven by a feedback loop (between annotators and QA) to improve the process.

After the completion of the project, the annotated data is converted to the client’s preferred format and delivered. We also conduct a post-project analysis to identify areas for improvement and refine our processes for future projects.

300+

Satisfied Clients
99.5%+ Accuracy
Customer Portfolios Optimized

Our annotation process is driven by a rigorous quality control mechanism to minimize errors and maximize the precision of machine learning models.

20%+ Faster Time-to-Market
Customers Benefit from AI-Enhanced Data

We bank on highly efficient workflows and a scalable team of experts to speed up project delivery and accelerate time-to-market.

30%+ Reduction in Annotation Costs
Annual Savings Through Digital Transformation

We harness custom tools to streamline processes to lower expenses significantly and ensure maximum savings for our clients.

10x Improvement in Model Performance
Quicker Real-Time Visual Data Processing

Our track record of producing high-quality LiDAR annotations guarantees improved model performance in object detection, classification, and segmentation tasks.

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