3 accepted papers
Although supervised deep stereo matching networks have made impressive achievements, the poor generalization ability caused by the domain gap prevents them from being applied to real-life scenarios. In this paper, we propose to leverage the feature of a model trained on large-scale datasets to deal…
Although convolutional neural network based stereo matching architectures have made impressive achievements, there are still some limitations: 1) Convolutional Feature (CF) tends to capture appearance information, which is inadequate for accurate matching. 2) Due to the static filters, current convo…
Self-supervised depth estimation has attracted a lot of attention recently due to its low cost. Despite using the self-supervision from image sequences, the current single-image based methods only infer depth from the scene information ignoring the matching information which is also important. Never