Dynamic 3D Gaussian Fields for Urban Areas

Understand

We present an efficient neural 3D scene representation for novel-view synthesis (NVS) in large-scale, dynamic urban areas.

• Existing works are not well suited for applications like mixed-reality or closed-loop simulation due to their limited visual quality and non-interactive rendering speeds.
• Recently, rasterization-based approaches have achieved high-quality NVS at impressive speeds.
• However, these methods are limited to small-scale, homogeneous data, i.e.