The digitization of cultural heritage artworks and crafts has become a critical strategy for their preservation, actively promoted by governments worldwide. One approach to this digitization is the generation of free-viewpoint images. These images, created through scene reconstruction based on multi-view image acquisition, not only enable detailed reproduction of intricately shaped crafts but also allow interpolation of unknown viewpoints, offering broad potential applications in fields such as education, tourism, and research. However, securing specialized personnel to manage the processes leading up to multi-view image acquisition remains a significant challenge. This research focuses on developing optimized environments for multi-view image acquisition by leveraging neural network-based methods. By defining environmental settings, such as camera arrangements, as known configurations, it becomes possible to easily acquire the necessary multi-view images without requiring specialized knowledge. By evaluating the reconstructed images, we propose shooting environments suitable for multi-view image acquisition, aiming to support the creation of digital content and enhance its utility.