Convert 2D Image to 3D Point Cloud Using OpenAI Point Cloud
Table of Contents
- Introduction
- What is Point Cloud?
- Point Cloud Generation from 2D Image
- Understanding Point Cloud Generation using Point Cloud
- How to Improve the Outcome of 3D Image
- Initializing the Model
- Defining the Sampler
- Uploading the Image
- Creating Samples
- Visualizing the Point Cloud
- Generating the Output 3D Image
- Example: Converting Chair Image to 3D
- Exporting the 3D Image
- Conclusion
Point Cloud Generation from 2D Image
Have You ever wondered how to convert a 2D image into a 3D point cloud? Well, wonder no more! In this tutorial, we will be exploring the world of point clouds and how to generate them from 2D images using Point Cloud, a new technology from OpenAI. We will be using Python code to walk through the entire process, and I will be sharing the Google Collab link in the YouTube description for you to play around with the code.
What is Point Cloud?
Point Cloud is a new technology from OpenAI that can Create 3D point clouds of an image or text. Unlike Dali, which is a 2D image generation technology, Point Cloud is the 3D successor of Dali. Point Cloud can create 3D point clouds of an image or text, and we have already covered text to 3D in this Channel. Today, we will be covering images to 3D.
Point Cloud Generation from 2D Image
To generate a point cloud from a 2D image, we will be using Point Cloud and Python code. The first thing we need to do is check whether we have GPU access. Then, we will install the library Plotly and the library Point Cloud, which will help us to create and Visualize the 3D image interactively.
Once we have done everything, we will import the necessary libraries for image manipulation, such as Fill, Torch, and Progress by TQDM, as well as everything related to Point Cloud. We will then initialize the model, which in this case is a base 40 model. You can change the model and use something else if you want to.
After we have downloaded and loaded the checkpoint, the next thing we need to do is define the sampler using the base model and the sampler model that we got. The sampler has certain important aspects that we need to keep in mind, such as the guidance Scale. The guidance scale and the model parameter that you have chosen will have a huge impact on the kind of output that you have.
The next step is for us to upload a file. Make sure that the image does not have a lot of items in it. For example, if you're going to upload an image, that image should have only one object in it and should have a plain background. This will help the algorithm take the object separately and then convert it into an image.
Once you have uploaded the right Type of image, the next thing is to load the image. The image file name will be displayed, and you can load the image and then start running the sampler. One thing that you would find different between the text to 3D model and the image to 3D or photo to 3D is in the model quarks. In the model quarks, you actually send the image.
At this point, the samples have been created and are ready and stored for us. We can just visualize the input image, which is what we gave, and then using the sampler, we can convert the samples into a point cloud. If you want to visualize the point cloud in 2D, like as a 2D image, you can see how the point cloud looks like. We will then generate the output 3D image.
Understanding Point Cloud Generation using Point Cloud
As you can see, the 3D image that we generated is not perfect. Hulk's head is not very clearly visible, and you can see it's still not perfect. However, this can literally take a few minutes of Python code and a very powerful model, which is Point Cloud, and a good computation power GPU that can help us convert the 2D image of Hulk into a 3D image, which we can export and then use it on tools like Blender.
How to Improve the Outcome of 3D Image
If you want to improve the outcome of the 3D image, there are a few things that you can do. First, make sure that the image you upload has only one object in it and has a plain background. This will help the algorithm take the object separately and then convert it into an image.
Second, play with the guidance scale. Anything from three to five to seven, whatever the value clips, play with the guidance scale. If you do not find a good result with the base 40m model, try out different options like the one billion parameter model if you have enough computation.
Initializing the Model
To initialize the model, We Are using a base 40 model. However, you can change the model and use something else if you want to. When you go to the Point Cloud Website, you can see what kind of models they are using, and then you can also play with the models. They've got different parameters of models.
Defining the Sampler
To define the sampler, we are using the base model and the sampler model that we got. The sampler has certain important aspects that we need to keep in mind, such as the guidance scale. The guidance scale and the model parameter that you have chosen will have a huge impact on the kind of output that you have.
Uploading the Image
To upload the image, make sure that the image does not have a lot of items in it. For example, if you're going to upload an image, that image should have only one object in it and should have a plain background. This will help the algorithm take the object separately and then convert it into an image.
Creating Samples
Once you have uploaded the right type of image, the next thing is to load the image. The image file name will be displayed, and you can load the image and then start running the sampler. One thing that you would find different between the text to 3D model and the image to 3D or photo to 3D is in the model quarks. In the model quarks, you actually send the image.
Visualizing the Point Cloud
At this point, the samples have been created and are ready and stored for us. We can just visualize the input image, which is what we gave, and then using the sampler, we can convert the samples into a point cloud. If you want to visualize the point cloud in 2D, like as a 2D image, you can see how the point cloud looks like.
Generating the Output 3D Image
We will then generate the output 3D image. The 3D image we're going to generate is going to be Based on the point cloud that we just created.
Example: Converting Chair Image to 3D
To demonstrate how to convert a chair image to 3D, we will be using the same process as before. We will upload the chair image, create samples, visualize the point cloud, and generate the output 3D image.
Exporting the 3D Image
To export the 3D image, we can take the image that we have got and then load a couple of utility functions that are shared by Point Cloud. We can use their model SDF model, and then finally, we can convert that into a mesh. That mesh we can save it as a .ply file for us to use in any 3D software like Open 3D and Blender.
Conclusion
In conclusion, Point Cloud is a new technology from OpenAI that can create 3D point clouds of an image or text. We have explored how to generate a point cloud from a 2D image using Point Cloud and Python code. We have also discussed how to improve the outcome of the 3D image and how to export the 3D image. If you have any questions, let me know in the comment section.
