Machine Learning In Interior Design: Uncovering New Possibilities

Computer vision has witnessed many applications in diverse and challenging fields ranging from assisting humans with their algorithms to sometimes outperform human beings. Nowadays, computer vision systems are making their way into human lives without even us knowing about it.

With the inception of 3D models in the 1960s, professionals in the field of computer engineering and automation used mathematical models and data analysis to build the first-ever 3D models. However, the pioneer of 3D graphics is Ivan Sutherland, the creator of Sketchpad, a program that made it possible to create first-ever 3D objects. Initially, 3D modelling found its application mostly in Television or advertisements, but with time, its presence in other areas of life increased.

We cannot imagine a world without automatic translations or virtual assistants since they are already so integral to our lives. In the past, the concept of a world that isn’t fully run by humans seemed as far-fetched as being able to walk on air. But, it is not so anymore. We are only beginning to see what deep learning can bring to the table in every other conceivable field like entertainment, industry regulations, marketing and finance, and customer experience.

With recent technological breakthroughs, we have just started to explore the use cases of Artificial Intelligence or “Narrow AI”. This comprises specific technologies like Deep Learning, Machine Learning, and 3D technologies like AR/VR. Human knowledge and experience, when coupled with creativity, give birth to great interior designs.

Semi-supervised learning algorithms use a combination of supervised and unsupervised learning techniques to train models because the data in the training set are both labeled and unlabeled. These algorithms are trained with both kinds of data to assign labels to the total (mostly unlabeled) data.

This journey from early-stage visual biometric systems to self-driving cars shows the promise the field of Computer Vision holds for the future which has constantly been seen as a highly automated one. To push this automation to the next levels, we need our machines to see, analyze and act more smartly.