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NextWealth / Services / Computer Vision / 3D Semantic Segmentation
Get pixel-level understanding of image for computer vision using Semantic Segmentation Services
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3D Semantic segmentation Overview
NextWealth provides highly accurate semantic segmentation with pixel-perfect precision that can help train AI/ML models more efficiently. Our extensive working experience with clients across various industries enables us to have a deeper understanding of the image from a predefined dataset. With our semantic segmentation deep learning services, you can only expect maximum accuracy.
What is Semantic Segmentation?
Semantic segmentation makes multiple elements in an image detectable by segmenting it to a specific class and localizing the object. A classification like this occurs when an image is categorized into two or more categories. Since such segmentation is done at the pixel level it gives better predictions and is used in advanced picture processing.
NextWealth provides semantic segmentation services to suit the needs of any computer vision model.
Types of Semantic Segmentation
Region-based semantic segmentation
This method uses free-form regions to separate the elements within an image. The selected region is converted into predictions at the pixel level to ensure that it is visible to the computer vision models.
Fully convolutional network-based semantic segmentation
This type of segmentation is used to extract meaningful information from the image. It is mainly used to perform tasks such as identification, recognition, and classification of objects and image processing.
Weakly supervised semantic segmentation
This type of segmentation is most commonly used to create pixel-wise segments from an image. The bounding boxes are used to train the models and customize various aspects as per the project needs.
Applications of 3D semantic segmentation
Fashion Industry
Semantic segmentation is utilized to distinguish the differences between various types of clothing items by AI models in the fashion industry. Such understanding of clothing by the models allows the creation of virtual wardrobes for customers who can try these clothes virtually on their screens.
Self-Driving Vehicles
Semantic segmentation helps in making the models understand the complex road environments and recognize different objects present. Such segmentation of the information not only divides pedestrians from roads but also ensures safety for everyone present on the road.
Satellite Image Processing
Semantic segmentation can train drones for geo-sensing of the fields and farms. Such precise segmentation is useful to enhance crop productivity and farming efficiency.
Biomedical Image Diagnosis
Segmentation of images in deep learning also provides accurate information in medical diagnosis through images. Such classification makes the diagnosis simple and generates faster results.
Face Recognition
The image segmentation method separates the face regions like eyes, nose, mouth, chin, and hair to make the computer vision model understand them. Such classification along with other factors like image resolution, feature occultation, etc. are used in gender prediction, facial expression, age recognition, and more.
Waste Management
Instance segmentation of images results in recognizing the individual waste type by the ML models. This results in quicker isolation and identification of the waste particles and eliminates the harm caused due to human handling of such dangerous materials.
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FAQs
What are the pros and cons of 2D and 3D semantic segmentation?
Both 2D and 3D semantic segmentation train the computer vision models to replicate the human ability to comprehend visuals and generate meaningful results.
However, there is a difference in the way each of them works and thereby their use cases. The 2D segmentation makes use of network architecture to achieve results and is hence used in satellite images. Whereas in 3D segmentation the objects are grouped in different categories and are hence are used in ADAS and 3D Maps.
As both 2D and 3D semantic segmentation enable machines to perform complex tasks they are utilized to obtain more granular level data resulting in better results.
What does semantic segmentation mean in "joint 3D reconstruction and semantic segmentation"?
To interpret the 3D world, any robot needs to understand its own position and the information for the surrounding 3D environment.
SLAM (Vision based simultaneous localization and mapping) is used to estimate the location of the robot. However, SLAM only reveals the structural data and hence the result is limited to a point. Scene parsing on the other hand reveals the pixel information in an image and categorizes them providing only semantic information.
But in certain use cases such as autonomous driving, it is important for the models to understand both semantic and structural information of the surroundings. Hence the 3D scene is presented in the form of boxes. Hence a joint 3D reconstruction and semantic segmentation of every frame within the image can result in a more realistic understanding.