Point-cloud-derived 2D projections enable generating unlimited virtual views for indoor scene analysis and dataset creation. However, projecting irregular 3D samples onto a dense image grid commonly produces void pixels due to sparsity, occlusions, and incomplete scan coverage. These projection-induced artifacts degrade the visual fidelity of rendered images and limit their usefulness in downstream image-based workflows. This study investigates void-filling strategies tailored to point-cloud-generated RGB projections and provides a comparative evaluation of three representative approaches: (i) K-nearest neighbor (KNN) interpolation with KD-Tree accelerated neighbor search, (ii) a rule-based neighborhood method (NNRule) that adapts filling behavior using local variability to preserve edges, and (iii) a mask-normalized Gaussian-weighted propagation method that diffuses valid color information into void regions. Experiments were conducted on multi-view perspective projections generated from Stanford Large-Scale 3D Indoor Spaces Dataset (S3DIS) Area 3, totalling 5,520 images. Restoration quality was assessed using standard pixel-level metrics such as MAE, RMSE, PSNR, and SSIM. Quantitative results show that Gaussian-weighted propagation achieved the best overall performance, followed by NNRule, while KNN performed weakest numerically. Qualitative comparisons further indicate that KNN produces the most visually realistic texture appearance, whereas diffusion-based filling is softened fine details. Finally, the study establishes a practical baseline that enables both academic researchers to advance point-cloud-to-image restoration without relying on paired RGB datasets and industrial practitioners to deploy light weight void-filling pipelines in real-world applications such as digital twins, indoor robotics, facility management, and augmented reality.