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About

Amir Rasouli is a computer vision and robotics researcher whose work spans autonomous systems, visual attention, and deep learning-based prediction. He is perhaps best known for his 2020 survey on deep learning for vision-based prediction, which synthesized five years of advances across applications including autonomous driving, surveillance, and human-robot interaction — earning 20 citations and establishing itself as a valuable reference for researchers navigating this rapidly evolving field. Earlier in his career, Rasouli made significant contributions to autonomous visual search, demonstrating in a 2014 study that incorporating visual saliency meaningfully improves robotic search efficiency, a finding that garnered 14 citations. He extended this line of work by integrating three complementary mechanisms of visual attention into a unified framework for robotic search (2017), and by exploring how task constraints shape sensor planning strategies in 3D environments (2016). More recently, Rasouli has turned his attention to zero-shot 6D pose estimation, introducing HIPPo, a model-free approach that leverages image-to-3D priors to estimate object poses without relying on CAD models or pre-posed reference images. Across these contributions, Rasouli's research consistently advances the autonomy and perceptual intelligence of robotic and vision systems.