Contrastive Hypothesis Selection for Robust and Accurate Multi-View Depth Refinement

CHOSEN, a simple yet flexible, robust and effective multi-view depth refinement framework, iteratively re-samples and selects the best depth hypotheses using contrastive learning, and automatically adapts to different metric or intrinsic scales determined by the capture system.