You will know more about the below 3 things when you finish reading this blog.
Today, eCommerce has been incorporating AI into everything such as enhancing the consumer experience, attracting users to accessing their platform, and increasing conversion rates.
Whether you talk about Personalized Recommendations, where Amazon’s “item-to-item collaborative filtering” algorithm is providing tailor-made content direct to the homepage which increased its sales or you talk about voice commands where Voice-activated assistants like Amazon’s Echo, Google Home, and the Apple HomePod, or on mobile devices with Cortana and Siri, AI is ruling the world. AI is a widely used technology in today’s time and industry tycoons like Alibaba, Rakuten, eBay, etc use it for various purposes ranging from product recommendations to fake reviews detection and managing big data. Gartner predicts that 85% of interactions between customers and retailers will happen “without interacting with a human” by 2020.
Despite going out of the way to deliver positive customer experiences, e-commerce stores still struggle to fulfill the product visualization expectations that customers have. It proves to be virtually impossible to offer product representations that are even remotely at par with how they are on-site in physical stores. This is where 3D technology can be leveraged to enable customers to visualize a product from all angles before making a purchase decision. With this, a customer can choose what part of the object they’d like to see, whether they want to zoom in or out the object or they want to rotate the object. All these aims can be realized using 3D modeling capabilities.
3 most common challenges for 3D models every eCommerce player is facing are –
SuperDNA realized this challenge earlier in the cycle and directly focused on AI experiments to integrate machine learning in creating 3D assets. We are investing in research and also creating our own AI/Machine Learning utilities which can help us to address these challenges. Below are some of the initiatives that we took –
Current Status of SuperDNA3D Lab – We started this in early 2018 and this utility is quite mature. We can save as much as 15% on efforts/costs which we pass on to our customers.
Technology background – We are using veggie, a CNN trained using Caffe deep learning framework. When you pass any image through the network it gives you pattern and shape features at the end of convolutional and fully connected (FC) layers, respectively. Those features are compressed using a compression matrix and then we use the nearest neighbor classifier to find the best match. The pattern and shape features of the database are pre-computed and saved in CSV files.
3D texture creation –
We also invested in scratch by creating low-resolution textures directly to the high-resolution texture which means if we have scanned resolution textures then we create directly high-resolution textures from our utilities. There are a lot of tools available online but they miss out on details and that’s what we are trying to address here.
3D model creation –
We are in the early stage of creating models from scratch for 3D directly from 2D images. We think if do a breakthrough in this utility then we will be able to solve most of the problems. Human intervention will still be needed but it will be a huge cost saving.
Technology Solution background –
SuperDNA is using an interesting approach to solve this problem. We came up with the idea of combining the research done by people with the traditional approach to solving any computer vision problem. Utilizing the research from Wu and Hane’s papers, we created 2 neural networks, first, one will be used to do the feature extraction and another network will predict the coordinate based on the features provided to this network. This approach is unique since we are combining 2 power networks, i.e., a Convolutional Neural Network(CNN) for feature extraction and a recurrent neural network for predicting coordinates of the 3D object.
How we are doing it?
In a nutshell –
To conclude, Integrating AI usage into 3D asset creation is a daunting task but the results are positive. By working on re-purposing and creation separately we can address challenges of scalability, quality, and costs.
Feel free to reach out to me if you want to know more …, Click here