CHOSEN: Contrastive Hypothesis Selection for Multi-View Depth Refinement
Di Qiu Yinda Zhang Thabo Beeler Vladimir Tankovich Christian Häne Sean Fanello Christoph Rhemann Sergio Orts Escolano
We propose CHOSEN, a simple yet flexible, robust and effective multi-view
depth refinement framework. It can be employed in any existing multi-view
stereo pipeline, with straightforward generalization capability for different
multi-view capture systems such as camera relative positioning and lenses.
Given an initial depth estimation, CHOSEN iteratively re-samples and selects
the best hypotheses, and automatically adapts to different metric or intrinsic
scales determined by the capture system. The key to our approach is the
application of contrastive learning in an appropriate solution space and a
carefully designed hypothesis feature, based on which positive and negative
hypotheses can be effectively distinguished. Integrated in a simple baseline
multi-view stereo pipeline, CHOSEN delivers impressive quality in terms of
depth and normal accuracy compared to many current deep learning based
multi-view stereo pipelines.