1 INTRODUCTION  
1.1 Scope  
1.2 Organization of the Manuscript  

2 TASK  

3 DATASETS AND EVALUATION  
3.1 Datasets  

4 FRAMEWORKS  
4.1 Taxonomy  
4.2 Methods Based on 2D Features  
4.3 Methods Based on 3D Features  

5 COMPARISON OF COMPONENTS  
5.1 Feature Extraction  
5.2 Result Prediction  
5.3 Loss Formulation  
5.4 Post-processing  

6 LEVERAGING AUXILIARY DATA  
6.1 LiDAR Signals  
6.2 Temporal Sequences  
6.3 Stereo Pairs  
6.4 Pre-Training Data  
6.5 Input Data as Taxonomy  

7 FUTURE DIRECTIONS  
7.1 Depth Estimation  
7.2 Multi-Modality  
7.3 Temporal Sequences  
7.4 Beyond Fully Supervised Learning  
7.5 Pre-Training  
7.6 Generalization  

8 CONCLUSIONS