Non-collaborative (i.e., reflective, transparent, metallic, etc.) surfaces are common in industrial production processes, where 3D reconstruction methods are applied for quantitative quality control inspections. Although the use or combination of photogrammetry and photometric stereo performs well for well-textured or partially textured objects, it usually produces unsatisfactory 3D reconstruction results on non-collaborative surfaces. To improve 3D inspection performances, this paper investigates emerging learning-based surface reconstruction methods, such as Neural Radiance Fields (NeRF), Multi-View Stereo (MVS), Monocular Depth Estimation (MDE), Gaussian Splatting (GS) and image-to-3D generative AI as potential alternatives for industrial inspections. A comprehensive evaluation dataset with several common industrial objects was used to assess methods and gain deeper insights into the applicability of the examined approaches for inspections in industrial scenarios. In the experimental evaluation, geometric comparisons were carried out between the reference data and learning-based reconstructions. The results indicate that no method can outperform all the others across all evaluations.

In the mid-1990s, the concept of smart growth emerged in the United States as a critical response to the phenomenon of suburban sprawl. To promote sustainable urban development, it is necessary to further investigate the principles and applications of smart growth. In this paper, we proposed a Smart Growth Index (SGI) as a standard for measuring the degree of responsible urban development. Based on this index, we constructed a comprehensive 3E evaluation model—covering economic prosperity, social equity, and environmental sustainability—to systematically assess the level of smart growth. For empirical analysis, we selected two medium-sized cities from different continents: Wuhu County, China, and Colima, Mexico. Using an improved entropy method, we evaluated the degree of smart growth in recent years and analyzed the contributions of various policies to sustainable urban development. Then, guided by the ten principles of smart growth, we linked theoretical insights to practical challenges and formulated a development plan for both cities. To forecast long-term trends, we employed trend extrapolation based on historical data, enabling the prediction of SGI values for 2020, 2030, and 2050. The results indicate that Wuhu demonstrates a greater potential for smart growth compared with Colima. We also simulated a scenario in which the population of both cities increased by 50 percent and then re-evaluated the SGI. The analysis suggests that while rapid population growth tends to slow the pace of smart growth, it does not necessarily exert a negative impact on the overall trajectory of sustainable development. Finally, a study on the application of Transit-Oriented Development (TOD) theory in Wuhu County was conducted. Based on this analysis, we proposed several policy recommendations aimed at enhancing the city’s sustainable urban development.