Submission name:

Tran & Khoshelham

Short description of the method:

The reconstruction is based on a shape grammar and a stochastic approach applicable to both Manhattan and non-Manhattan world buildings. The building interior is first partitioned into a set of 3D shapes as an arrangement of permanent structures. An optimization process is then applied to search for the most probable model as the optimal configuration of the 3D shapes using the reversible jump Markov Chain Monte Carlo (rjMCMC) sampling with the Metropolis-Hastings algorithm. This optimization is not based only on the input data, but also takes into account the intermediate stages of the model during the modelling process.

Reference:

Tran, H. and Khoshelham, K., 2019. A Stochastic Approach to Automated Reconstruction of 3D Models of Interior Spaces from Point Clouds. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-2/W5, pp. 299-306.

Tran, H. and Khoshelham, K., 2020. Procedural Reconstruction of 3D Indoor Models from Lidar Data Using Reversible Jump Markov Chain Monte Carlo. Remote Sensing, 12, 838.

URL:

https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-2-W5/299/2019/

https://www.mdpi.com/2072-4292/12/5/838

Submission date:

1 Nov. 2019

Last update:

1 Nov. 2019