Designing computer vision solutions for home interior applications is complex, as gathering diverse and accurate data can be expensive, time consuming, and riddled with privacy concerns. Read on to find out how Unity’s synthetic dataset generation tools and services enable the development of more capable computer vision applications for the home while mitigating roadblocks and challenges.
One of the most challenging aspects of building performant computer vision (CV) models is curating datasets with sufficient diversity and accurate labeling. Recently, synthetic datasets have brought about viable solutions to these issues by minimizing the need for costly and time consuming acquisition and annotation of real data.
Unity has been at the forefront of this shift to synthetic data. One of the key areas where our customers have sought our expertise in building synthetic training datasets are home interiors. These involve a variety of applications in home automation, security, assistive technologies, healthcare, pet and baby monitoring, home interior design, and more.
For home interior applications, it is particularly challenging to acquire labeled datasets based on real homes due to privacy concerns. This is compounded by training datasets requiring a high degree of diversity in elements such as materials, colors, lighting, and furniture.
To help these customers, we have been developing tools and 3D content libraries for generating photorealistic home environments. To achieve a high level of variation in the data, we use procedural furniture placement and numerous randomized elements, such as camera positions, materials, lighting, time of day, sky and outdoor environment, and placement of additional custom objects into the environment. The image below depicts examples for four of these randomizations.
Source: Unity Technologies Blog