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About

Or Litany is a researcher whose work spans neural representations, robotics, and 3D visual computing, with particular focus on bridging cutting-edge machine learning with practical spatial understanding. He is perhaps best known for his foundational contributions to the field of **neural fields** — coordinate-based neural networks that parameterize physical properties of scenes and objects across space and time. His survey paper "Neural Fields in Visual Computing and Beyond" (2022) has become a landmark reference in the field, amassing over 447 citations and helping define the conceptual vocabulary for an entire research community. Beyond neural representations, Litany has made meaningful contributions to embodied AI and robotics through his ReLMoGen framework, which innovatively integrates motion generation into reinforcement learning for mobile manipulation tasks. By elevating the action space to motion subgoals rather than low-level joint controls, this work demonstrated a more scalable and efficient approach to continuous robot control, earning over 120 combined citations across its iterations. His more recent work on fast monocular scene reconstruction explores efficient hybrid grid representations, addressing real-world demands from augmented reality and robotics applications. Earlier contributions to shape correspondence using functional maps further reflect the breadth of his geometric deep learning expertise, establishing him as a versatile and impactful voice in modern visual computing research.