Data collection, particularly ground-truth generation, is crucial for developing computer vision models used for construction progress monitoring applications. The performance of such models relies heavily on the quality of the data, which drives the effectiveness of machine learning algorithms. If data is not collected and subsequently managed correctly, the algorithms will fail, and the applicability of these models for construction monitoring applications will be degraded. In the absence of quality data, synthetic image generation using BIM has been widely studied to resolve data insufficiency issues. Because of the domain gap between synthetic and real-world images, most recent works have focused on rendering techniques to enhance the realism of lighting and texture. However, the impact of extrinsic camera parameters, which directly influence how BIM elements are rendered in camera views, is heavily underexplored. This leads to an over-utilization of weakly created synthetic ground-truth images. As a result, these images and their often-random camera position and viewpoints fail to reflect real-world visual perspectives needed for enterprise-grade solutions for monitoring construction progress. To improve the quality of synthetic construction environment datasets, this paper explores the integration of per-element visibility metrics to understand how different positional camera parameters impact the synthetic data collection pipeline and segmentation model performance improvement. This work is validated by comparing real-image segmentation accuracy through experiments using visibility metrics from different camera positions and directions. Finally, a discussion of how positional camera parameters can be selected for producing a more efficient and less biased synthetic dataset is presented.