Steve Nadis | MIT CSAIL
June 17, 2022
Upon looking at photographs and drawing on their past experiences, humans can often perceive depth in pictures that are, themselves, perfectly flat. However, getting computers to do the same thing has proved quite challenging.
The problem is difficult for several reasons, one being that information is inevitably lost when a scene that takes place in three dimensions is reduced to a two-dimensional (2D) representation. There are some well-established strategies for recovering 3D information from multiple 2D images, but they each have some limitations. A new approach called “virtual correspondence,” which was developed by researchers at MIT and other institutions, can get around some of these shortcomings and succeed in cases where conventional methodology falters.
Complete article from MIT News.
Explore
New Method Could Increase LLM Training Efficiency
Adam Zewe | MIT News
By leveraging idle computing time, researchers can double the speed of model training while preserving accuracy.
New Tool Makes Generative AI Models More Likely to Create Breakthrough Materials
Zach Winn | MIT News
With SCIGEN, researchers can steer AI models to create materials with exotic properties for applications like quantum computing.
Photonic Processor Could Enable Ultrafast AI Computations with Extreme Energy Efficiency
Adam Zewe | MIT News
This new device uses light to perform the key operations of a deep neural network on a chip, opening the door to high-speed processors that can learn in real-time.




