Have you ever wondered how to turn a simple 2D image into a complex 3D model? Using a tool like DUST3R can make this process incredibly accessible. Here’s a walkthrough of a recent project where I used DUST3R to generate a 3D model and then used bounding boxes to pinpoint specific objects within that 3D space.
Step 1: Creating a 3D Model with DUST3R
The first step in this project was creating a 3D model from a set of 2D images. I used DUST3R, a model that can predict the camera poses and depth maps for a set of input images. This process is a form of photogrammetry, where multiple photos taken from different angles are used to reconstruct a 3D scene.
I started by taking several photos of an object from various viewpoints. I then fed these images into DUST3R. The tool analyzed the images, identified matching features, and calculated the relative position and orientation of the camera for each shot. It then generated a dense point cloud, which is essentially a collection of data points in 3D space that represents the surface of the object. This point cloud formed the foundation of my 3D model.
Step 2: Bounding Boxes in the 2D Image
With the 3D model generated, the next challenge was to identify specific objects within the scene. I used bounding boxes (BBox), which are a common feature in computer vision for object detection. A bounding box is a simple rectangular coordinate that encloses a specific object in a 2D image.
In this case, I drew bounding boxes around the objects of interest in my original 2D images. For example, in an image of a table with a coffee cup on it, I would draw one bounding box around the table and another around the coffee cup. This step provided a way to identify and segment the objects I wanted to analyze further.
Step 3: Plotting 3D Center Points from 2D Pixels
The most critical part of this project was bridging the gap between the 2D bounding boxes and the 3D model. The goal was to take the center point of each 2D bounding box and find its corresponding location in the 3D space created by DUST3R.
This process involves a concept known as reprojection, where we transform 2D pixel coordinates into 3D world coordinates. Since DUST3R provided the camera’s intrinsic parameters (like focal length) and extrinsic parameters (its 3D position and orientation), I could use these to convert the 2D pixel coordinates of the bounding box’s center into a 3D ray extending from the camera. Where this ray intersects with the reconstructed 3D surface of the model is the estimated 3D location of the object’s center point.
By doing this for each bounding box, I was able to get a precise 3D coordinate for the center of each object in the scene. This not only allows for better understanding and analysis of the reconstructed scene but also opens the door for applications like augmented reality, where you could overlay digital information onto the 3D model in the correct real-world locations.