Our Approaches
We leverage synthetic data and advanced machine learning techniques to develop robust pose estimation models. Our 3D rendering capabilities allow us to simulate various poses and conditions, while generative AI adds realistic details to these synthetic images. This comprehensive approach ensures our models can accurately estimate poses under diverse and challenging conditions.
We begin by analysing the specific pose estimation needs of our clients, including the types of poses and scenarios they encounter. Using CAD models and advanced rendering techniques, we generate synthetic datasets that accurately represent these conditions. Our generative AI techniques add realistic details such as lighting variations, textures, and occlusions, ensuring the models are well-equipped to handle real-world challenges.
Our approach also includes continuous model validation and optimization, using both synthetic and real-world data. This ensures that our pose estimation systems remain accurate and reliable over time. We provide tools for easy integration with existing workflows, enabling seamless adoption and deployment of our solutions.
