no code implementations • 22 Oct 2023 • Ashkan Ganj, Yiqin Zhao, Hang Su, Tian Guo
In this paper, we investigate the challenges and opportunities of achieving accurate metric depth estimation in mobile AR.
1 code implementation • 15 Jan 2023 • Yiqin Zhao, Chongyang Ma, Haibin Huang, Tian Guo
In this work, we present the design and implementation of a lighting reconstruction framework called LitAR that enables realistic and visually-coherent rendering.
no code implementations • 15 Jan 2023 • Yiqin Zhao, Sean Fanello, Tian Guo
This lack of support can be attributed to the unique challenges of obtaining 360$^\circ$ HDR environment maps, an ideal format of lighting representation, from the front-facing camera and existing techniques.
1 code implementation • 30 May 2021 • Yiqin Zhao, Tian Guo
Centering the key idea of 3D vision, in this work, we design an edge-assisted framework called Xihe to provide mobile AR applications the ability to obtain accurate omnidirectional lighting estimation in real time.
1 code implementation • ECCV 2020 • Yiqin Zhao, Tian Guo
We propose an efficient lighting estimation pipeline that is suitable to run on modern mobile devices, with comparable resource complexities to state-of-the-art mobile deep learning models.