Occlusion is a common challenge in indoor 3D scanning, often leading to incomplete scene reconstructions due to unobserved surfaces. Estimating it during the scan process can improve the quality of the acquired scenes tremendously. However, most existing methods for estimating scan completeness require access to ground-truth data, an assumption that rarely holds in practical settings. We introduce OCCAM (Occlusion-aware Completeness via Coverage Analysis with Monte Carlo sampling), a lightweight method that estimates global scan coverage without requiring surface reconstruction. It casts randomized rays from within the scanned volume to identify visibility gaps, without relying on mesh connectivity or external reference geometry. In contrast to occupancy grid mapping methods, which model local space coverage from the scanner's perspective, OCCAM evaluates broader scene visibility to detect whether large surface regions remain unscanned. Experimental results on synthetic and real-world benchmark datasets show that the proposed method is fast to compute (processing 100K-point scans in under one second), simple to implement, and produces a compact signal that supports both coverage assessment and scan guidance in indoor environments.
Perez et al. (Wed,) studied this question.