Exploiting BIM Objects for Synthetic Data Generation toward Indoor Point Cloud Classification Using Deep Learning

Frías, Ernesto; Pinto, José; Sousa, Ricardo B.; Lorenzo, Henrique; Díaz-Vilariño, L.

doi:10.1061/(asce)cp.1943-5487.0001039

Cited by 5 publications

(2 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To investigate the value synthetic data have for use as training data and thus the added value they can bring to scan-to-BIM toolchains, several related contributions are relevant for this work: Frías et al (2022) used BIM objects to generate synthetic point clouds by sampling, to then render them to images and use them for object classification. For the application in historical buildings, Morbidoni et al (2020) used synthetic, sampling-based point cloud data generated based on structural components of available 3D models to train an adapted version of DGCNN (dynamic graph convolutional neural network, Y.…”

Section: Synthetic Training Data For Semantic Segmentationmentioning

confidence: 99%

“…To investigate the value synthetic data have for use as training data and thus the added value they can bring to scan‐to‐BIM toolchains, several related contributions are relevant for this work: Frías et al. (2022) used BIM objects to generate synthetic point clouds by sampling, to then render them to images and use them for object classification. For the application in historical buildings, Morbidoni et al.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing point cloud semantic segmentation in the data‐scarce domain of industrial plants through synthetic data

Noichl,

Collins,

Braun

et al. 2024

Computer aided Civil Eng

View full text Add to dashboard Cite

Digitizing existing structures is essential for applying digital methods in architecture, engineering, and construction. However, the adoption of data‐driven techniques for transforming point cloud data into useful digital models faces challenges, particularly in the industrial domain, where ground truth datasets for training are scarce. This paper investigates a solution leveraging synthetic data to train data‐driven models effectively. In the investigated industrial domain, the complex geometry of building elements often leads to occlusions, limiting the effectiveness of conventional sampling‐based synthetic data generation methods. Our approach proposes the automatic generation of realistic and semantically enriched ground truth data using surface‐based sampling methods and laser scan simulation on industry‐standard 3D models. In the presented experiments, we use a neural network for point cloud semantic segmentation to demonstrate that compared to sampling‐based alternatives, simulation‐based synthetic data significantly improve mean class intersection over union performance on real point cloud data, achieving up to 7% absolute increase.

show abstract

Section: Synthetic Training Data For Semantic Segmentationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%