Abstract
Given the technical requirements for equipment storage in the power industry, conventional storage methods prove inadequate for managing specialized materials such as precision instruments. To address texture distortion and detail loss in 3D visualization of such equipment, this study establishes a fused rendering algorithm integrating Dynamic Weighted Linear Interpolation (DWLI) and Adaptive Texture Mapping (ATM). The innovative contributions are threefold in several aspects. Firstly, we propose a Triple-Feature Descriptor (TFD) that categorizes surface characteristics across three orthogonal dimensions: geometric structure, material properties, and photometric attributes. This approach enhances feature discriminability by 32.7% compared to conventional methods. Secondly, our curvature-adaptive sampling strategy dynamically adjusts resolution (scalable from 256 × 256 to 1024 × 1024), improving edge fidelity in precision components (e.g., miniature relays) by 19.3 dB PSNR. Thirdly, the designed mapping architecture maintains color accuracy below ΔE < 3.2 across illuminance levels of 50-1000 lx. Validation on the MDPI power warehouse dataset demonstrates 91.4% material identification accuracy with real-time rendering at 28 fps (RTX 3060). Deployment at a State Grid hub warehouse elevated sorting throughput by 40% and slashed manual verification workload by 65%. Comparative evaluations against conventional Texture Mapping methods reveal performance improvements exceeding 10% in both algorithm precision and recall rates. These advancements contribute substantially to operational efficiency in warehouse management systems, particularly facilitating cross-temporal domain material allocation optimization and resource coordination. The research outcomes provide practical solutions for addressing technical challenges in modern power industry logistics management.