Doc: nerf.studio
Neural radiance fields (NeRFs) are rapidly gaining popularity for their ability to create photorealistic 3D reconstructions in real-world settings, with recent advances driving interest from a wide variety of disciplines in academia and industry. However, due to the flux of papers, consolidating code has been a challenge, and few tools exist to easily run NeRFs on user-collected data. I'll introduce nerfstudio, an open-source Python framework we recently released to address these issues by consolidating NeRF research innovations and making NeRFs easier to use in real-world applications. I'll discuss its development and recent updates such as visual effects integrations and more.
Bio: Angjoo Kanazawa ([ Ссылка ]) is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of California at Berkeley. Her research is at the intersection of Computer Vision, Computer Graphics, and Machine Learning, focusing on the visual perception of the dynamic 3D world behind everyday photographs and video. Previously, she was a research scientist at Google NYC, and prior to that she was a BAIR postdoc at UC Berkeley. She completed her PhD in Computer Science at the University of Maryland, College Park, where she also spent time at the Max Planck Institute for Intelligent Systems. She has been named a Rising Star in EECS and is a recipient of Anita Borg Memorial Scholarship, Best Paper Award in Eurographics 2016, Google Research Scholar Award 2021, Spark Fellow 2022, and Sloan Fellow 2023. She also serves on the advisory board of Wonder Dynamics, whose goal is to utilize AI technologies to make VFX effects more accessible for indie filmmakers, and PainScan, whose goal is to make chronic pain visible in 3D so they can be properly diagnosed and treated.
