Accurate point cloud data is vital for the up-to-date representation of buildings. The use of Terrestrial Laser Scanners (TLS) has been prevalent for generating precise point clouds in the built environment. However, despite the development of scan planning algorithms to address issues such as occlusions and varying point densities, the significant costs and extensive time required for data acquisition and processing—often worsen by the lack of existing plans for scan planning—limit the practicality and cost-effectiveness of TLS. This necessitates the investigation of alternative methods for point cloud data collection. This study assesses the capabilities and limitations of low-cost solid-state LiDAR sensors, specifically the Livox Mid-360, in mapping the built environment. The sensor’s performance was benchmarked against TLS and onsite measurements, testing its mapping accuracy over distances ranging from 2 m to 8 m and on different materials including steel, wood, plastic, and concrete. The results demonstrate that the sensor achieves sub-centimeter accuracy within a 2 m range, with the highest accuracy on wood and the least on steel. These results validate the efficacy of low-cost solid-state LiDAR sensors for building modeling requiring centimeter-level accuracy, offering a more affordable and accessible solution for updating Building Information Modelling (BIM) model.