Semantic Scholar Open Access 2018 257 sitasi

LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image

Chuhang Zou Alex Colburn Qi Shan Derek Hoiem

Lihat Sumber DOI

Abstrak

We propose an algorithm to predict room layout from a single image that generalizes across panoramas and perspective images, cuboid layouts and more general layouts (e.g. "L"-shape room). Our method operates directly on the panoramic image, rather than decomposing into perspective images as do recent works. Our network architecture is similar to that of RoomNet [15], but we show improvements due to aligning the image based on vanishing points, predicting multiple layout elements (corners, boundaries, size and translation), and fitting a constrained Manhattan layout to the resulting predictions. Our method compares well in speed and accuracy to other existing work on panoramas, achieves among the best accuracy for perspective images, and can handle both cuboid-shaped and more general Manhattan layouts.

Topik & Kata Kunci

Computer Science

Penulis (4)

Chuhang Zou

Alex Colburn

Qi Shan

Derek Hoiem

Format Sitasi

APA MLA BibTeX

Zou, C., Colburn, A., Shan, Q., Hoiem, D. (2018). LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image. https://doi.org/10.1109/CVPR.2018.00219

Akses Cepat

Lihat di Sumber doi.org/10.1109/CVPR.2018.00219

Informasi Jurnal

Tahun Terbit: 2018
Bahasa: en
Total Sitasi: 257×
Sumber Database: Semantic Scholar
DOI: 10.1109/CVPR.2018.00219
Akses: Open Access ✓