AEC Solutions for the Future, Today
Unlocking Innovative Solutions in Architecture, Engineering and Construction
Unlocking Innovative Solutions in Architecture, Engineering and Construction
At DigitalFish, we bring advanced tools, immersive visualization technologies, and decades of experience in 3D content production to deliver groundbreaking solutions for the AEC industry.
Our expertise allows us to deliver innovative solutions that also help you save money through accurate preconstruction planning, resource optimization, and predictive analytics.
DigitalFish empowers AEC professionals to visualize and execute projects with precision. Whether you are designing a new skyline or managing the intricate details of a construction site, we can help integrate the latest technology into your existing processes. From widely used visualization engines such as Unreal Engine and NVIDIA Omniverse, to emerging industry standards for 3D content such as OpenUSD and OpenXR, to new devices including Apple Vision Pro and Meta Quest, DigitalFish brings world-class expertise and a lengthy resume of past work.
With over a decade of experience applying artificial intelligence to creative and production processes, DigitalFish is a trusted partner of some of the world’s largest platform and device companies. From computer vision to generative AI, DigitalFish helps our customers integrate the latest AI tools for creating, sharing and experiencing content.
For a Big Tech industry partner, DigitalFish built a cloud pipeline to generate a large library of 3D virtual environments, automating the modularization of 6+ million 3D assets and over 10 thousand unique Unreal Engine 5 scenes. We used this library to create an even larger-scale synthetic dataset of high-fidelity visual walkthroughs and associated ground-truth data: image segmentation, object pose, and auto-generated labeling. This dataset is used in training advanced machine-learning models for scene understanding.
Training on very large synthetic datasets dramatically increases model accuracy and enables the training to include rare and unsafe features that don’t occur readily or would be unfeasible to create in real-world data. Synthetic data is also far faster and cheaper to generate than collecting and labeling real-world data.
The result is safer, more-dependable models.