BUNCH offers experienced annotators proficient in LiDAR annotation, handling various labeling techniques such as semantic annotation, 3D cuboid/box annotation, landmark annotation, polygon annotation, and polyline annotation. Following industry-proven processes, BUNCH provides consistent quality n LiDAR annotation services for diverse industries and applications.
Train your machine learning models for computer vision, autonomous vehicles, and environmental monitoring applications. We can annotate datasets at scale to support the development of robust and accurate ML models.
LiDAR annotation is the process of labeling 3D point cloud data, essential for training machine learning models in various applications. Annotation quality is crucial for output datasets, as it directly impacts the accuracy and performance of the resulting structured data models.
BUNCH guarantees high-quality data by employing double-pass annotation audits, implementing strict QA processes, and involving subject matter experts in the loop. We carefully analyze the annotation guidelines prior to project commencement and refine them in collaboration with our clients. Learning from the dataset, we make iterative improvements to enhance the overall annotation quality.
To avoid data inconsistencies and obtain high-quality datasets for training robust and reliable ML models, our outsourcing Lidar annotation services employ several strategies that have a significant impact on data quality while maintaining optimal efficiency:
Before starting production, we run a calibration test on a representative sample, using preliminary annotation guidelines provided by the client. Thishelps us estimate the average handle time and familiarize ourselves with the dataset's complexity and potential ambiguities.
We collaborate with clients to develop comprehensive Lidar annotation guidelines, including class definitions, object libraries, edge cases, examples, and escalation triggers. These guidelines are iteratively improved as we learn from actual data and unanticipated edge cases.
Our expert annotators undergo a deep learning stage to internalize the dataset's details. This involves studying the documentation and assessing their capacity to resolve edge cases through client-specific exams.
Each Lidar annotation is audited by an independent QA analyst who scores the work using a scorecard. Annotators with low scores undergo retraining, while those with high scores are rewarded and progress to higher roles over time.
Projects are divided into smaller batches and delivered separately depending on the dataset volume. Recurrent projects involve daily, weekly, or monthly delivery packages.
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Get a custom plan with elastic pricing models that fit your annotation project volume, quality and urgency
Our elastic workforce allows you to scale up from thousands to millions of images in hours
Continuous training and rigorous QA complemented by double-pass techniques secure the highest accuracy
Our fully managed in-house annotation teams enable pixel-perfect accuracy and full compliance with your guidelines
We will annotate a sample of your images and come back to you with proposed productivity estimates and quality thresholds
Our exposure to different dashboards enables us to handle high-volume and multi-user annotation at exceptional efficiency standards
We permanently delete your datasets upon completion of milestones. Our in-house team is under strict NDA to protect your business confidentiality.
We meet international compliance standards for data handling and processing, security, confidentiality, and privacy
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We set full-time teams and work on one-time projects of all sizes.