The image depicts a large dry erase board with various images and text explaining 3D model generation. The board is divided into several sections, with diagrams and text explaining different aspects of the process. The images appear to be of various objects and landscapes, and the text provides detailed explanations of the techniques used to create them. The board is mounted on a wall, and there are several people standing in front of it, likely studying or discussing the material. The room appears to be a classroom or a workshop, with a few chairs and a desk visible in the background.
Text transcribed from the image:
UNIVERSITY
oft
OF AMSTERDAM
Zuoyue Li Zhenqiang Li Zhaopen
en or predicted geometry and render
sistency
•
Why 3D generation?
Consistency naturally holds
Do not need preset trajectory
Why diffusion models
instead of GANs?
Better performance
Stability during training
4 Experiment
•
Baseline comparison Method/Metric
HoliCity dataset
GT geometry
Various metrics
Sat2 Vid
InfiniCity
MVDiffusion
Ours
w/ different generative models
3D GAN-based method
= 2D GAN-based method
ion: 2D diffusion-model-based method
Rendered
Images
B
Generated background
on Model
Point cloud
w/ feature
M
Sat.
Ground-view
Bird-view
MVDiff.
Ground-view
Model generalization
OmniCity data
ture
Factor
Renderer
Rendering
Ours
Bird-view