Team Members

RAIS4BE Lab was established at National University of Singapore in 2021. We are a multidisciplinary team with backgrounds in civil, mechanical, geodesy and computer engineering. Our work focuses on building robotics, autonomous sensing, and AI-assisted digital building modelling to support reality capture in complex built environments, including legacy buildings, infrastructure and industrial facilities. We develop high-precision Scan-to-BIM and semantic enrichment methods to support digital twin reconstruction, design optimisation and the future construction of high-performance, buildable structural systems.

Shuju Jing

Research Fellow

Joining

Research Fellow

Difeng (PhD/Postdoc)

Difeng Hu

PhD Researcher

Tao Wang

PhD Researcher

Qiao Zheng

PhD Researcher

Kexin Li

PhD Researcher

Josh Li

PhD Researcher

Srivatsan

PhD Researcher

Xiayi Chen

MSc (Research)

Oh Hui Lin

MSc (Research)

Yushuo Wang

MSc (Research)

Runfeng Ma

MSc (Research)

Dian Zhuang

Visiting PhD

Chao Xiang

Visiting PhD

Ben Ben

Lab Companion

Scout

Lab Companion

Mike Li

Research Fellow

Vincent Gan

Assistant Professor

Asiri

Research Associate

Shaobo Li

Research Associate

Yuanyuan Deng

Research Assistant

Jey Chandar

Research Assistant

Melanie Tan

PhD Researcher

Xiuqi Li

PhD Researcher

Bi Jian

Visiting PhD

Ruoming Zhai

Visiting PhD

Robot Dog & LiDAR Scanning (2022)

Automated 3D Mapping & Semantic Navigation

Trajectory Optimisation & 3D Perception (2024)

Our Research & Teaching

CDE Innovation Day Award

Teaching Excellent Award

Featured Publication

Deng, Y.Y., Gan, V.J.L.,* • Advanced Engineering Informatics (4.2026)

Motion-prior and Confidence-aware Gaussian Splatting (MCGS) SLAM for 3D scene reconstruction of indoor built environments

This paper proposes a motion-prior and confidence-aware Gaussian Splatting (MCGS) SLAM, which hardnesses a probabilistic motion-prior framework, confidence estimation mechanism, and adaptive keyframe selection to guide photorealistic 3D scene reconstruction.

Zheng, Q., Gan, V.J.L.,* Li, M.K., • Automation in Construction (1.2026)

Semantic instance segmentation and automated 3D BIM reconstruction for viaduct using LiDAR point clouds and weakly-supervised learning

This paper presents an AI-based semantic instance segmentation approach that leverages weakly-supervised learning for high-precision segmentation and automated BIM reconstruction of transport infrastructure, focusing on viaducts.

Li, M.K., Gan, V.J.L.,* Wang, B.Y., • Automation in Construction (11.2025)

Integrating hierarchical segmentation and vision-language reasoning for spatially complex and occluded MEP point clouds

This paper proposes a hierarchical and progressive segmentation that integrates deep learning-based semantic segmentation, geometry-driven instance segmentation, and vision-language model-assisted refinement for 3D BIM reconstruction of MEP systems.

Hu, D.F., Gan, V.J.L.,* • Automation in Construction (3.2025)

Semantic navigation for automated robotic inspection and indoor environment quality monitoring

This paper proposes a semantic navigation approach to improve robotic inspection. A revised RandLA-Net and KNN algorithm construct a semantic map rich in detailed object information. An object instance reasoning algorithm identifies and extracts target object coordinates from the semantic map. A semantics-aware A* algorithm calculates safer, efficient navigation paths.

Gan, V.J.L., Hu, D.F.,* etc. • Computer-Aided Civil and Infrastructure Engineering (3.2025)

Automated indoor 3D scene reconstruction with decoupled mapping using quadruped robot and LiDAR sensor

This study introduces an optimization algorithm incorporating viewpoint generation, occlusion detection and culling, and robot-moving trajectory identification. The research investigates 3D reconstruction, comparing coupled and decoupled approaches to identify most practical configuration for robotic scanning.

Gan, V.J.L., Li, K.X.,* etc. • Applied Energy (1.2025)

3D reconstruction of BIM with weakly-supervised learning for carbon emission modelling in the built environment

This paper presents weakly-supervised learning for automated BIM reconstruction, aiming at accurate carbon performance evaluation. By employing weakly-supervised semantic segmentation, this approach segments structural components from 3D point clouds and formulates the topological relationships of objects for BIM reconstruction to assess embodied carbon.

Zhai, R., Zou, J., Gan, V.J.L.,* etc. • Automation in Construction (10.2024)

Semantic enrichment of BIM with IndoorGML for quadruped robot navigation and automated 3D scanning

In this paper, BIM data schema is enriched with IndoorGML, integrating building geometry with spatial data to establish an indoor navigation model describing multi-scale spatial topological networks. This navigation model optimizes robot scanning positions and traversal sequences.

Wang, T., Gan, V.J.L.,* • Automation in Construction (10.2024)

Enhancing 3D reconstruction of textureless indoor scenes with IndoReal multi-view stereo

This paper presents the “IndoReal-MVS” dataset, a rich indoor-centric compilation reflecting real-world phenomena through advanced computer graphics. It introduces unsupervised “IndoorMatchNet”, synergising Feature Pyramid Network (FPN) and Pyramid Flowformer (PFF) for encoding complex indoor geometries.

Hu, D., Gan, V.J.L.,* etc. • Building and Environment (8.2022)

Multi-agent robotic system (MARS) for UAV-UGV path planning and automatic sensory data collection in cluttered environments

This paper presents a multi-agent robotic system for automatic UAV-UGV path planning and indoor navigation to automate sensory data collection. An enhanced shunting short-term memory model is proposed to optimise the pathfinding, 2D image and 3D point cloud data collection.

Gan, V.J.L.,* • Automation in Construction (2.2022)

BIM-based graph data model for automatic generative design of modular buildings

This paper presents a Building Information Modelling (BIM)-based graph data model for the theoretic representation of spatial attributes, topological relationships, geometries, and semantics for generative design of modular buildings.

Research Highlight

Robotic Reality Capture and Automated 3D Scanning: Our research on robotic 3D scanning focuses on sensor perception models, 3D scene reconstruction and understanding algorithms, semantic mapping and navigation methods to support automated quality-assured data acquisition in semi-structured environments. This research potentially saves the time and manpower for data acqusition.

  • Developed sensor perception models and environment-aware trajectory optimisation for assuring robotic scan data quality

  • Multi-view, GS-SLAM, and domain adaption algorithms for 3D scene reconstruction and understanding in weakly-textured environments

  • Robot semantic 3D perception, navigation and autonomous LiDAR mapping in semi-structured environments

  • Our team members are pursuing Scan2BIM robot to support the digitalisation and diagnosis of buildings and infrastructures

AI-assisted Scan-to-BIM and Digital Twin Reconstruction: Building on robotic 3D data acquisition, our research aims to develop different geometric representation learning pipelines to transform 3D spatial data into structured, semantically-enriched BIM models aligned with building foundation model requirements. Key highlights are summarised below:

  • Developed weakly-supervised learning, VLM or zero-shot SAM, and BIM-synthetic point cloud pipelines for semantic instance segmentation of spatially complex industrial facilities and infrastructural components

  • DR-GANet and knowledge-based inferencing to process heterogeneous point clouds and automated BIM reconstruction

  • Semantic similarity matching and relationship classification for semantic enrichment of BIM data to generate engineering models

Generative Design Optimisation and Digital Fabrication: Beyond 3D reconstruction, our research further investigates graph-based AI and broad pre-training of 3D BIM geometry to capture the structural priors and support the optimisation of topologically valid and buildable structural systems for digital fabrication. Below are the research highlights:

  • Proposed BIM-based graph data model to integrate building spatial, geometric & topological features

  • Developed deep learning and bio-inspired generative design algorithms to optimise building shape, structural topology and element sizing, capturing both structural integrity and constructability requirement

  • Automated BIM-CAM data exchange and IFC-based tracking to support prefabrication of reinforced concrete components