破解空间智能数据稀缺难题，影石开源DiT架构全景生成模型，在线可玩

Core Insights - The article discusses the introduction of DiT360, a panoramic image generation model based on the Diffusion Transformer (DiT) architecture, which addresses the scarcity of high-quality panoramic data in the field of spatial intelligence [2][11][50]. Group 1: DiT360 Model Overview - DiT360 utilizes a hybrid training framework that combines limited panoramic data with a large volume of high-quality perspective images, significantly enhancing both realism and geometric consistency in generated images [4][12][50]. - The model is capable of generating high-resolution panoramic images (2048×1024) across various environments, demonstrating superior detail and realism compared to existing methods [11][30]. Group 2: Challenges in Panoramic Image Generation - Generating panoramic images involves overcoming geometric challenges such as seamless stitching and polar distortion, compounded by the scarcity and quality limitations of real panoramic data [8][9][10]. - Existing approaches either break panoramic images into multiple planar views or generate them directly on a spherical surface, both of which face issues with boundary consistency and distortion [9][10]. Group 3: Training Mechanisms - DiT360 employs a multi-level hybrid training mechanism that enhances the diversity and realism of generated results through image-level and feature-level strategies [12][17]. - The image-level approach includes panorama refinement and perspective image guidance to improve the structural quality of panoramic data and facilitate cross-domain knowledge transfer [14][16]. Group 4: Performance Evaluation - DiT360 outperforms various state-of-the-art methods in visual quality and geometric consistency, achieving leading scores across multiple evaluation metrics [30][32][36]. - User studies indicate that DiT360 is preferred for realism and overall quality, with preference rates of 63.8% and 80.9%, respectively, significantly higher than competing methods [38][39]. Group 5: Future Applications - The hybrid training strategy of DiT360 can be extended to applications such as panoramic video generation, VR/AR content creation, and dynamic scene simulation, enhancing the realism and spatial consistency of generated scenes [51][52].