no code implementations • 10 Nov 2022 • Chi-Chen Lee, Po-Tsun Paul Kuo, Chi-Han Peng
We also compared to StainNet and found that our method delivered quantitatively and qualitatively better results.
no code implementations • 20 Oct 2022 • Jheng-Wei Su, Chi-Han Peng, Peter Wonka, Hung-Kuo Chu
The major improvement over PSMNet comes from a novel Geometry-aware Panorama Registration Network or GPR-Net that effectively tackles the wide baseline registration problem by exploiting the layout geometry and computing fine-grained correspondences on the layout boundaries, instead of the global pixel-space.
no code implementations • 19 Oct 2022 • Chi-Han Peng, Jiayao Zhang
We propose a novel approach to compute high-resolution (2048x1024 and higher) depths for panoramas that is significantly faster and qualitatively and qualitatively more accurate than the current state-of-the-art method (360MonoDepth).
3 code implementations • 9 Oct 2019 • Chuhang Zou, Jheng-Wei Su, Chi-Han Peng, Alex Colburn, Qi Shan, Peter Wonka, Hung-Kuo Chu, Derek Hoiem
Recent approaches for predicting layouts from 360 panoramas produce excellent results.
1 code implementation • CVPR 2019 • Shang-Ta Yang, Fu-En Wang, Chi-Han Peng, Peter Wonka, Min Sun, Hung-Kuo Chu
We present a deep learning framework, called DuLa-Net, to predict Manhattan-world 3D room layouts from a single RGB panorama.