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. However%2C loss functions applied for deep MVS are rarely studied. In this paper%2C we first analyze existing loss functions' properties for deep depth based MVS approaches. Regression based loss leads to inaccurate continuous results by computing mathematical expectation%2C while classification based loss outputs discretized depth values. To this end%2C we then propose a novel loss function%2C named adaptive Wasserstein loss%2C which is able to narrow down the difference between the true and predicted probability distributions of depth. Besides%2C a simple but effective offset module is introduced to better achieve sub-pixel prediction accuracy. Extensive experiments on different benchmarks%2C including DTU%2C Tanks and Temples and BlendedMVS%2C show that the proposed method with the adaptive Wasserstein loss and the offset module achieves state-of-the-art performance.?quality=80&w=800)
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