Monocular 3-D Reconstruction of Blast Furnace Burden Surface Based on Cross-Domain Generative Self-Supervised Network

Accurate acquisition of the 3-D topography of the blast furnace (BF) burden surface is crucial for optimizing the ironmaking process. However, traditional methods struggle in the high temperature, high pressure, and dusty environment of the BF top, and even advanced industrial endoscopes only capture monocular images, limiting multiview stereoscopic reconstruction. To address these challenges, we propose a novel 3-D reconstruction framework featuring a virtual–real multiview endoscope array for capturing multiview images and a cross-domain point cloud generation self-supervised network (XGSN). The XGSN leverages a progressive multimodal self-attention mechanism and ray-tracing projection to compensate for the lack of 3-D labels, producing a high-fidelity 3-D point cloud. Experimental results show that the proposed method achieves a significant improvement in burden surface reconstruction accuracy, delivering high-quality 3-D mapping with enhanced real-time processing capabilities, demonstrating its potential for challenging industrial environments.