no code implementations • 24 Dec 2023 • Bailey Miller, HanYu Chen, Alice Lai, Ioannis Gkioulekas
Starting from a stochastic representation of opaque solids as random indicator functions, we prove the conditions under which such solids can be modeled using exponential volumetric transport.
1 code implementation • 24 Aug 2023 • Dakshit Agrawal, Jiajie Xu, Siva Karthik Mustikovela, Ioannis Gkioulekas, Ashish Shrivastava, Yuning Chai
We propose a novel-view augmentation (NOVA) strategy to train NeRFs for photo-realistic 3D composition of dynamic objects in a static scene.
no code implementations • CVPR 2023 • Byeongjoo Ahn, Michael De Zeeuw, Ioannis Gkioulekas, Aswin C. Sankaranarayanan
Full-surround 3D reconstruction is critical for many applications, such as augmented and virtual reality.
no code implementations • CVPR 2023 • Adithya Pediredla, Srinivasa G. Narasimhan, Maysamreza Chamanzar, Ioannis Gkioulekas
We introduce a light steering technology that operates at megahertz frequencies, has no moving parts, and costs less than a hundred dollars.
no code implementations • CVPR 2023 • Alankar Kotwal, Anat Levin, Ioannis Gkioulekas
We introduce an interferometric technique for passive time-of-flight imaging and depth sensing at micrometer axial resolutions.
no code implementations • 17 Sep 2022 • Mohamad Qadri, Michael Kaess, Ioannis Gkioulekas
We present a technique for dense 3D reconstruction of objects using an imaging sonar, also known as forward-looking sonar (FLS).
no code implementations • CVPR 2023 • Alankar Kotwal, Anat Levin, Ioannis Gkioulekas
As in conventional synthetic wavelength interferometry, our technique uses light consisting of two narrowly-separated optical wavelengths, resulting in per-pixel interferometric measurements whose phase encodes scene depth.
no code implementations • CVPR 2022 • Ryan Po, Adithya Pediredla, Ioannis Gkioulekas
Single-photon avalanche diodes (SPADs) are growing in popularity for depth sensing tasks.
no code implementations • ICCV 2021 • Shumian Xin, Neal Wadhwa, Tianfan Xue, Jonathan T. Barron, Pratul P. Srinivasan, Jiawen Chen, Ioannis Gkioulekas, Rahul Garg
We use data captured with a consumer smartphone camera to demonstrate that, after a one-time calibration step, our approach improves upon prior works for both defocus map estimation and blur removal, despite being entirely unsupervised.
no code implementations • 28 Sep 2018 • Chengqian Che, Fujun Luan, Shuang Zhao, Kavita Bala, Ioannis Gkioulekas
We introduce inverse transport networks as a learning architecture for inverse rendering problems where, given input image measurements, we seek to infer physical scene parameters such as shape, material, and illumination.
no code implementations • CVPR 2015 • Ioannis Gkioulekas, Bruce Walter, Edward H. Adelson, Kavita Bala, Todd Zickler
We also discuss the existence of shape and material metamers, or combinations of distinct shape or material parameters that generate the same edge profile.