HAPNet: hierarchically aggregated pyramid network for real-time stereo matching
HAPNet: hierarchically aggregated pyramid network for real-time stereo matching
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DOI：
10.1080/21681163.2020.1835561
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发表时间：
2020-11-26
影响因子：
1.6
通讯作者：
Janatka, Mirek
中科院分区：
文献类型：
--
作者：
Brandao, Patrick;Psychogyios, Dimitris;Janatka, Mirek
恢复手术部位的3D形状对于多种计算机辅助干预至关重要。立体内窥镜可用于计算3D深度，但计算立体是一个具有挑战性的、非凸的和固有不连续的优化问题。在本文中，我们提出了一种深度学习架构，该架构避免了显式构建相似性成本量，这是立体声算法中计算成本最高的块之一。这使得训练我们的网络更加有效，并避免了大量内存分配的需求。我们的方法表现良好，特别是在手术器械周围或复杂的小结构和器械周围包含多个不连续性的区域周围。该方法相比，以及国家的最先进的技术，同时采取不同的方法学角度，在手术视频中的计算立体声问题。
Recovering the 3D shape of the surgical site is crucial for multiple computer-assisted interventions. Stereo endoscopes can be used to compute 3D depth but computational stereo is a challenging, non-convex and inherently discontinuous optimisation problem. In this paper, we propose a deep learning architecture which avoids the explicit construction of a cost volume of similarity which is one of the most computationally costly blocks of stereo algorithms. This makes training our network significantly more efficient and avoids the needs for large memory allocation. Our method performs well, especially around regions comprising multiple discontinuities around surgical instrumentation or around complex small structures and instruments. The method compares well to the state-of-the-art techniques while taking a different methodological angle to computational stereo problem in surgical video.