Floor-plan generation from noisy point clouds

Xin Liu; Egor Bondarev; Peter H. N. de With

doi:10.1117/12.2679358

7 June 2023 Floor-plan generation from noisy point clouds

Xin Liu, Egor Bondarev, Peter H. N. de With

Proceedings Volume 12701, Fifteenth International Conference on Machine Vision (ICMV 2022); 1270111 (2023) https://doi.org/10.1117/12.2679358
Event: Fifteenth International Conference on Machine Vision (ICMV 2022), 2022, Rome, Italy

Abstract

This paper proposes a growing-based floor-plan generation method that creates the global layout of buildings from noisy point clouds obtained by a stereo camera. We introduce a PCA-based line-growing concept with a subsequent filtering step, which is able to robustly handle the high noise levels in input point clouds. Experimental results show that this method outperforms the state-of-the-art techniques in floor-plan generation. The average F₁ score for building layouts has increased from 0.38 to 0.66 on our test dataset, compared to the previous best floor-plan generation method. Furthermore, the resulting floor plans are multiple thousands of times smaller in memory size than the input point clouds, while still preserving the main building structures.

Citation Download Citation

Xin Liu, Egor Bondarev, and Peter H. N. de With "Floor-plan generation from noisy point clouds", Proc. SPIE 12701, Fifteenth International Conference on Machine Vision (ICMV 2022), 1270111 (7 June 2023); https://doi.org/10.1117/12.2679358

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