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

Abstract 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 sepᵣepair 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.