Ming Qian, Zimin Xia, Changkun Liu, Shuailei Ma, Wen Wang, Zeran Ke, Bin Tan, Hang Zhang, Gui-Song Xia

• [Jan 29, 2025] Repository initialized.
• Code Release: The source code is currently being organized and refined. We expect to complete the cleanup and release it officially before the conference starts. Thank you for your patience and interest!

Generating a street-level 3D scene from a single satellite image is a crucial yet challenging task. Current methods present a stark trade-off: geometry-colorization models achieve high geometric fidelity but are typically building-focused and lack semantic diversity. In contrast, proxy-based models use feed-forward image-to-3D frameworks to generate holistic scenes by jointly learning geometry and texture, a process that yields rich content but coarse and unstable geometry.

We introduce Sat3DGen to address these fundamental challenges, embodying a geometry-first methodology. This methodology enhances the feed-forward paradigm by integrating novel geometric constraints with a perspective-view training strategy, explicitly countering the primary sources of geometric error. This geometry-centric strategy yields a dramatic leap in both 3D accuracy and photorealism. We demonstrate the versatility of our high-quality 3D assets through diverse downstream applications, including semantic-map-to-3D synthesis, multi-camera video generation, large-scale meshing, and unsupervised single-image Digital Surface Model (DSM) estimation.

• Project Page Launch
• Online Demo
• 3D Visualization Tools
• Release Pre-trained Models
• Release Inference Code
• Release Evaluation Scripts
• Release Data Processing Pipeline