An efficient method for generating orthographic dense depth maps from point clouds in industrial scenarios

In industrial visual measurement, converting point clouds into depth maps is a widely adopted technique to enhance data processing efficiency and structural representation. However, the process is plagued by voids and structural distortions arising from non-uniform sampling, occlusions, and projection ambiguities.To address these issues, we propose an efficient method for generating orthographic dense depth maps. The method's novelty lies in three key contributions: a visibility-prioritized preprocessing framework to suppress depth distortion, a robust depth fusion strategy to resolve projection ambiguities, and a composite inpainting algorithm to effectively restore void regions. Extensive experiments validate our method's state-of-the-art performance. For the task of generating orthographic depth maps, our framework improves the Chamfer Distance by up to 14.38% compared to the commercial platform VisionMaster. For the critical sub-task of depth completion, our sep_repair algorithm demonstrates superior robustness over the recent SOTA deep learning method, LRRU. In the most challenging "Severe missing" scenarios—where the deep learning model's performance degrades sharply—our method achieves a 23.87% reduction in RMSE while completing the task in seconds.Furthermore, our entire framework achieves this SOTA-level performance efficiently on a standard CPU, highlighting its practical applicability for edge devices in smart manufacturing without the need for training data or GPU acceleration.