How can combine yolo and monocular depth estimation like Depth-Anything to make vector space (3D), like Tesla ?

[damavand1]

Hi ultralytics team

How can combine yolo and monocular depth estimation like Depth-Anything to make vector space like Tesla ?

Is any project that combine this two ?

[glenn-jocher]

Hello!

Combining YOLO with monocular depth estimation to create a 3D vector space is indeed an exciting application! While we don't have a specific project that combines these two directly, you can achieve this by running YOLO for object detection and a separate depth estimation model in parallel. After obtaining the bounding boxes from YOLO and depth information, you can merge these data points to construct a 3D representation of the scene.

Here's a very basic outline of how you might approach this:

1. Object Detection with YOLO:
   Run YOLO to detect objects in the scene and get their bounding boxes.

   from ultralytics import YOLO
   model = YOLO('yolov8n.pt')
   results = model('image.jpg')

2. Depth Estimation:
   Use a monocular depth estimation model like Depth-Anything on the same image to get depth information.

   # Pseudo-code for depth estimation
   depth_model = DepthModel('depth_model.pt')
   depth_map = depth_model('image.jpg')

3. Combine Data:
   Merge the bounding box information from YOLO with the depth information to create a 3D vector space representation.

   # Pseudo-code for combining data
   for box in results.boxes:
       center_x, center_y = box.xyxy[0], box.xyxy[1]
       depth = depth_map[center_y, center_x]
       # Use depth and box information to construct 3D space

While this is a simplified view, it outlines the core steps you'd follow. For a real-world application, you'd need to handle coordinate transformations and possibly calibrate the depth data based on your camera setup.

Feel free to explore and experiment with this approach! 🚀