According to @godofprompt on Twitter, the new Depth Anything 3 model introduces a breakthrough in 3D computer vision by leveraging a single vanilla transformer without complex architectures. This AI system reconstructs full 3D geometry from any number of images—whether single or multiple, posed or unposed—outperforming previous state-of-the-art (SOTA) models like VGGT across all geometry benchmarks. Practical results show a 35.7% improvement in pose accuracy and a 23.6% increase in geometric accuracy, with monocular depth estimation that surpasses DA2. The model simplifies the 3D pipeline by using a minimal setup of depth and per-pixel rays, eliminating the need for multi-task training or point-map tricks. A key innovation is the teacher-student learning approach, where a robust synthetic teacher model aligns noisy real-world data to produce clean, dense pseudo-labels, enabling the transformer to learn human-like visual space understanding. This advance opens new business opportunities for scalable, universal 3D perception models in robotics, AR/VR, autonomous vehicles, and digital twins, offering significant reductions in engineering complexity and resource requirements (Source: @godofprompt, Twitter, Nov 18, 2025; Paper: Depth Anything 3: Recovering the Visual Space from Any Views).