Lotus: A Diffusion-based Visual Foundation Model for Dense Geometry Prediction
Practical Solutions and Value:
Dense geometry prediction in computer vision is crucial for robotics, autonomous driving, and augmented reality applications. Lotus, a novel model, improves accurate geometry prediction without extensive training. It handles diverse tasks such as Zero-Shot Depth and Normal estimation, using diffusion processes for flexibility and adaptability.
Lotus generates detailed geometry predictions by transforming images through noise-added stages, capturing rich geometric details often missed by traditional CNN-based models. It excels in zero-shot scenarios, showcasing state-of-the-art performance in tasks like Zero-Shot Depth and Normal estimation.
Lotus offers user-friendly tools for exploration, including interactive Gradio applications. It represents a significant advancement in dense geometry prediction, providing a powerful and flexible solution for various visual prediction tasks.
If you aim to evolve your company with AI, consider Lotus for dense geometry prediction. Identify automation opportunities, define KPIs, select suitable AI solutions, and implement gradually to stay competitive and enhance your work processes.
Connect with us at hello@itinai.com for AI KPI management advice and stay updated on leveraging AI by following us on Telegram t.me/itinainews or Twitter @itinaicom.
