LGT-Net: Indoor Panoramic Room Layout Estimation with Geometry-Aware Transformer Network

Ming Zhao, Zhigang Jiang, Zhongzheng Xiang, Jinhua Xu

arXiv · PDF · Open in the Atlas

Code that ran

We lifted 41 functions out of this paper's own repositories and ran 29 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.

Repositories linked to this paper

• sunset1995/HorizonNet
• zhigangjiang/LGT-Net
• zillow/zind

Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.

Abstract

3D room layout estimation by a single panorama using deep neural networks has made great progress. However, previous approaches can not obtain efficient geometry awareness of room layout with the only latitude of boundaries or horizon-depth. We present that using horizondepth along with room height can obtain omnidirectionalgeometry awareness of room layout in both horizontal and vertical directions. In addition, we propose a planargeometry aware loss function with normals and gradients of normals to supervise the planeness of walls and turning of corners. We propose an efficient network, LGT-Net, for room layout estimation, which contains a novel Transformer architecture called SWG-Transformer to model geometry relations. SWG-Transformer consists of (Shifted) Window Blocks and Global Blocks to combine the local and global geometry relations. Moreover, we design a novel relative position embedding of Transformer to enhance the spatial identification ability for the panorama. Experiments show that the proposed LGT-Net achieves better performance than current state-of-the-arts (SOTA) on benchmark datasets. The code is publicly available at https: //github.com/zhigangjiang/LGT-Net.

For agents

The same record, over MCP at https://syntology.ai/mcp:

get_harvested_code_for_paper("2203.01824")
get_code_for_paper("2203.01824")
have("2203.01824")

Connect an agent — have() is free.