Extracting insight from 3D point cloud data has historically been a frustratingly slow and tedious process. It presents unique challenges that break traditional machine learning frameworks, and it requires highly trained individuals who are in short supply. Enview’s team of experts have developed a technology-first approach that provides a highly extensible framework for rapidly solving complex 3D challenges as part of a digital twin ecosystem. Our technology has been deployed at nation-scale to solve mission critical challenges ranging from vegetation management for Fortune 500 utility companies, automating property analysis for insurance and disaster recovery applications, and logistics and mobility applications for national security customers.
Analyzing point clouds and other 3D datasets is a highly manual process. Previously, it has been difficult to automate these tasks because of the novel complications of unstructured 3D data that cause traditional 2D machine learning approaches to fail. Our unique approach and expertise in sensor-fused 3D data analytics has allowed us to automate tasks previously thought too complex to automate.
Leveraging vanguard computer vision techniques and novel convolutional kernels, Enview has built an ML framework that can rapidly deliver custom trained AI models targeted to solve a customer’s specific needs for 3D object recognition, semantic segmentation, and computational geometry analyses.
Automation needs to be capable of handling enterprise scale. Enview is built with a team of in-house experts who specialize in the intersection of massive scale high-performance computing and point cloud data. We take on work dealing with nation-scale datasets doing critical analysis for the most sensitive of clients.
— San Gunawardana, CEO Enview