Cuboid Annotation – Annotating 3D Objects with 2D Data
Building a 3D representation of the world from 2D images is one of the major concerns in Computer Vision. The initial step that helps to achieve this is annotating 3D objects with 2D data using Cuboids.
What is Cuboid Annotation?
Cuboid Annotation is the task of labeling objects in 2D images with cuboids. The 3D cuboids help to determine the depth of the targeted objects such as vehicles, humans, buildings, etc.
Cuboid Annotation is used for building a 3D simulated world from 2D information captured by cameras. The 3D cuboidal training data helps to train the Cuboid Detection models which aid in localizing the objects of interest in the world and in estimating their pose.
Cuboid Annotation helps autonomous vehicles to understand the real-world environment. It mainly aids in detecting vehicle movement and its dimension for autonomous vehicles. It helps the self-driving cars to measure the distance of each obstacle from the vehicle and calculate the spacing.
Identifying Indoor Objects
You can build the model perception for detecting indoor objects using the 3D cuboid annotated images. This helps to train your Computer Vision models to have in-depth object detection capabilities. Cuboid Annotation helps to identify indoor objects like couch, table and other furniture with precision and best quality.
Cuboid Annotation can be effectively used for training robots that are deployed in different industries such as automotive, warehousing, etc. It helps to create better perception models that enable the robots to work continuously without the need for human interference. The 3D Cuboid Annotation of images captured from 2D cameras, powers perception of the robots and drone imagery which have applications in various fields.
At Data Labeler, our main focus is on creating quality labeled datasets for Machine Learning and Artificial Intelligence initiatives. We offer a diverse range of services which include Bounding Boxes for object detection, polygons for semantic and instance segmentation, key points for facial and body pose detection, image captioning texts, and image classification.