A power-efficient engine that can disentangle the visual attributes of objects
Most humans are innately able to identify the individual attributes of sensory stimuli, such as objects they are seeing, sounds they are hearing, and so on. While artificial intelligence (AI) tools have become increasingly better at recognizing specific objects in images or other stimuli, they are often unable to disentangle their individual attributes (e.g., their color, size, etc.).
Deep convolutional neural networks (DCNNs) don't see objects the way humans do -- using configural shape perception -- and that could be dangerous in real-world AI applications. The study employed novel visual stimuli called 'Frankensteins' to explore how the human brain and DCNNs process holistic, configural object properties.
We present a novel differentiable rendering framework for joint geometry, material, and lighting estimation from multi-view images. In contrast to previous methods which assume a simplified environment map or co-located flashlights, in this work, we formulate the lighting of a static scene as one neural incident light field (NeILF) and…