1 INTRODUCTION

2 SCOPE AND TAXONOMY

3 DATASETS

4 DEPTH BY STEREO MATCHING
4.1 Learning feature extraction and matching
4.2 Regularization and disparity estimation

5 END-TO-END DEPTH FROM STEREO
5.1 Feature learning
5.2 Cost volume construction
5.3 Disparity computation
5.4 Variants
5.5 Learning confidence maps

6 LEARNING MULTIVIEW STEREO
6.1 Volumetric representations
6.2 Plane-Sweep Volume representations

7 TRAINING END-TO-END STEREO METHODS
7.1 Supervision methods
7.2 Incorporating additional cues
7.3 Domain adaptation and transfer learning
7.4 Learning the network architecture

8 DISCUSSION AND COMPARISON
8.1 Evaluation protocol
8.2 Computation time and memory footprint
8.3 Reconstruction accuracy

9 FUTURE RESEARCH DIRECTIONS