论文标题
半监督的深度基线同型估计和进行性等价约束
Semi-supervised Deep Large-baseline Homography Estimation with Progressive Equivalence Constraint
论文作者
论文摘要
由于图像覆盖层较低，并且接受场有限，同构估计是错误的。为了解决这个问题，我们通过将大型基线同构象转换为多个中间体来提出一种渐进估算策略，累积乘以这些中间项目可以重建初始同型。同时，引入了半监督同构象身份损失，由两个组成部分组成：一个监督目标和无监督的目标。第一个监督的损失是采取优化中间同构的行动，而第二个无监督的损失有助于估计没有光度损失的大型基线同型。为了验证我们的方法，我们提出了一个涵盖常规场景的大规模数据集。实验表明，我们的方法在大型基线场景中实现了最先进的表现，同时在小基线场景中保持竞争性能。代码和数据集可在https://github.com/megvii-research/lbhomo上找到。
Homography estimation is erroneous in the case of large-baseline due to the low image overlay and limited receptive field. To address it, we propose a progressive estimation strategy by converting large-baseline homography into multiple intermediate ones, cumulatively multiplying these intermediate items can reconstruct the initial homography. Meanwhile, a semi-supervised homography identity loss, which consists of two components: a supervised objective and an unsupervised objective, is introduced. The first supervised loss is acting to optimize intermediate homographies, while the second unsupervised one helps to estimate a large-baseline homography without photometric losses. To validate our method, we propose a large-scale dataset that covers regular and challenging scenes. Experiments show that our method achieves state-of-the-art performance in large-baseline scenes while keeping competitive performance in small-baseline scenes. Code and dataset are available at https://github.com/megvii-research/LBHomo.
