VASAD: a Volume and Semantic dataset for Building Reconstruction from Point Clouds

Abstract

3D scene reconstruction has important applications to help to produce digital twins of existing buildings. While the community has mostly focused on surface reconstruction or semantic segmentation as separate problems, the joint reconstruction of both volumes and semantics has little been discussed, mostly due to the lack of large scale volume datasets with semantic annotations. In this work, we introduce a new dataset called VASAD for Volume And Semantic Architectural Dataset. It is composed of 6 building models, with full volume description and semantic labels. It approximately represents 62,000 m 2 of building floors, making it large enough for the development and evaluation of learning-based approaches. We propose several methods to jointly reconstruct both geometry and semantics and evaluate on the test set of the dataset. We show that the proposed dataset is challenging enough to stimulate research. The dataset is available at https://github.com/palanglois/vasad.

Semantic Point Cloud Segmentation with Deep-Learning-Based Approaches for the Construction Industry: A Survey

“…Table 1 features synthetic datasets and singular attempts that utilize synthetic training data to improve real-world performance. An outstanding example is the VASAD dataset [22] for building reconstruction. Researchers argue that the transfer of knowledge can improve the ability to perform complex tasks when initially performed in simulation [181,182].…”

“…The dataset offers RGB-D video streams and 3D camera poses of 1.5K residential scenes captured with low-cost sensor setups and crowd-sourced instance-level semantic annotation. VASAD [22] is a synthetic volume and semantic architectural dataset composed of six buildings. The focus of VASAD is to improve semantic segmentation and volumetric reconstruction for BIM modeling.…”

“…Within the civil engineering domain, only a few papers covered the use of deep learning to perform PCSS. Some remarkable publications automated as-built BIM generation [19,20], building reconstruction [21,22], and bridge part semantic segmentation [23,24]. Nevertheless, up to now, there is no settled trend for the civil engineering branch, and most approaches only feature outdated PointNet network architectures.…”