Complete depth for images using sparse depth maps
Decode images to teacher model outputs
Identify objects in images using ResNet
Multimodal Language Model
Identify and classify objects in images
Generate depth map from an image
Restore blurred or small images with prompt
Colorize grayscale images
Generate depth map from an image
Find similar images using tags and images
Swap faces in images
FitDiT is a high-fidelity virtual try-on model.
Enhance and upscale images, especially faces
Marigold Depth Completion is an advanced AI-powered tool designed to complete depth information in images using sparse depth maps. It leverages cutting-edge technology to generate dense depth maps, enhancing the depth information from limited input data. This tool is particularly useful for applications in computer vision, robotics, and 3D reconstruction, where accurate depth perception is crucial.
• AI-Powered Depth Estimation: Utilizes sophisticated AI models to infer missing depth information from sparse depth maps.
• High Accuracy: Produces highly accurate depth maps, even from limited input data.
• Efficiency: Optimized for fast processing while maintaining quality.
• Versatility: Works with various input formats, including RGB-D images.
• User-Friendly: Designed for ease of use, with minimal input requirements.
Pro Tip: For best results, ensure the sparse depth map has sufficient coverage of the scene to allow the AI to accurately infer the missing depth information.
1. What input formats does Marigold Depth Completion support?
Marigold Depth Completion supports standard image formats like PNG, JPG, and depth maps in common formats such as CSV or NumPy files.
2. Can Marigold Depth Completion work with low-quality or noisy sparse depth maps?
Yes, Marigold Depth Completion is designed to handle low-quality or noisy sparse depth maps, though accuracy may vary depending on the quality of the input.
3. What are the typical use cases for Marigold Depth Completion?
Marigold Depth Completion is commonly used in 3D reconstruction, autonomous systems, and augmented reality applications where accurate depth perception is essential.