For autonomous cars, the most important sensor operates at higher autonomy levels (L4-L5). Deep learning algorithms needstons of trained data that can be annotated using point clouds. At some instances, due to the low resolutionand length of the process, intricate annotation procedures are not sufficient. Beyond this,annotating with the 3D point cloud data is most convenient. Mei Infotech, on the other hand, is the One-Stop Solution on performing this task with picture annotation services to provide training data for machine learning algorithms.
Cloud annotation permits the annotation of small objects as small as 1 cm marking it at every position. The sensitivity of the sensors, depends on the ability to view small objects also. This is easily achieved using 3D point cloud annotation since it makes objects recognisable both in indoor and outdoor settings. Our in house trained human work force is capable of annotating any form of objects and generate datasets using appropriate techniques and methods.
3D point cloud segmentation is a method of categorizing an object with additional features that any perception model easily learns and train the machines. For example, self-driving cars needs 3D orientation also for safer driving. This 3D point cloud annotation renders the facility of sensing different sorts of lanes in 3D cloud map road.
Services for Data Annotation for Computer Vision Training Sets
Data Annotation enables the machines to learn and train datasets with the highest level of accuracy by confronting international standards of data quality
Consistent and constant sourcing of streaming data is one of the industrial demands for the machines to learning and training of the real time environments. The scalability of the work force and economic challenges the growth and fulfil the project completion. We have well trained and experienced work force to execute and implement the demanding solutions of all situations
We gained strong trust and confidence of our clients on the privacy, security and confidentiality of data in work execution by adhering to the international data security, privacy and confidentiality norms.