# The Direction-Aware, Learnable, Additive Kernels and the Adversarial Network for Deep Floor Plan Recognition

Source: [The Direction-Aware, Learnable, Additive Kernels and the Adversarial Network for Deep Floor Plan Recognition](<https://www.sota2.com/research/paper/68fa67011e209131cea13b3e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:04:19.938Z

Authors: Yuli Zhang, Yeyang He, Shaowen Zhu, Xinhan Di

Published: 2020-01-30T07:10:39.000Z

## Abstract

This paper presents a new approach for the recognition of elements in floor plan layouts. Besides of elements with common shapes, we aim to recognize elements with irregular shapes such as circular rooms and inclined walls. Furthermore, the reduction of noise in the semantic segmentation of the floor plan is on demand. To this end, we propose direction-aware, learnable, additive kernels in the application of both the context module and common convolutional blocks. We apply them for high performance of elements with both common and irregular shapes. Besides, an adversarial network with two discriminators is proposed to further improve the accuracy of the elements and to reduce the noise of the semantic segmentation. Experimental results demonstrate the superiority and effectiveness of the proposed network over the state-of-the-art methods.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2001.11194>)

[PDF](<https://arxiv.org/pdf/2001.11194.pdf>)