1 code implementation • ICCV 2023 • Ruojin Cai, Joseph Tung, Qianqian Wang, Hadar Averbuch-Elor, Bharath Hariharan, Noah Snavely
Our evaluation shows that our method can distinguish illusory matches in difficult cases, and can be integrated into SfM pipelines to produce correct, disambiguated 3D reconstructions.
1 code implementation • CVPR 2023 • Haotong Lin, Qianqian Wang, Ruojin Cai, Sida Peng, Hadar Averbuch-Elor, Xiaowei Zhou, Noah Snavely
Specifically, we represent the scene as a space-time radiance field with a per-image illumination embedding, where temporally-varying scene changes are encoded using a set of learned step functions.
1 code implementation • ICCV 2023 • Qianqian Wang, Yen-Yu Chang, Ruojin Cai, Zhengqi Li, Bharath Hariharan, Aleksander Holynski, Noah Snavely
We present a new test-time optimization method for estimating dense and long-range motion from a video sequence.
1 code implementation • CVPR 2021 • Ruojin Cai, Bharath Hariharan, Noah Snavely, Hadar Averbuch-Elor
We present a technique for estimating the relative 3D rotation of an RGB image pair in an extreme setting, where the images have little or no overlap.
1 code implementation • CVPR 2021 • Le Yang, Haojun Jiang, Ruojin Cai, Yulin Wang, Shiji Song, Gao Huang, Qi Tian
Reusing features in deep networks through dense connectivity is an effective way to achieve high computational efficiency.
1 code implementation • ECCV 2020 • Ruojin Cai, Guandao Yang, Hadar Averbuch-Elor, Zekun Hao, Serge Belongie, Noah Snavely, Bharath Hariharan
Point cloud generation thus amounts to moving randomly sampled points to high-density areas.