论文标题
使用3D表面感知约束的3D内窥镜深度估计
3D endoscopic depth estimation using 3D surface-aware constraints
论文作者
论文摘要
机器人辅助手术允许外科医生通过立体视觉和灵活的运动控制进行精确的手术手术。但是，缺乏3D空间感知限制了过程中的情况意识，并阻碍了狭窄的腹部空间中的手术技能。深度估计作为代表性的感知任务通常被定义为图像重建问题。在这项工作中，我们表明可以从3D表面的角度对深度估计进行改革。我们为深度估计提出了损失函数，该损失函数整合了表面感知的约束，从而使来自空间信息的有效信息更快，更好地收敛。此外，将摄像机参数纳入训练管道中，以提高深度估计的控制和透明度。我们还集成了一个镜面去除模块，以恢复更多掩埋的图像信息。内窥镜数据集和医学专业人员的用户研究的定量实验结果证明了我们方法的有效性。
Robotic-assisted surgery allows surgeons to conduct precise surgical operations with stereo vision and flexible motor control. However, the lack of 3D spatial perception limits situational awareness during procedures and hinders mastering surgical skills in the narrow abdominal space. Depth estimation, as a representative perception task, is typically defined as an image reconstruction problem. In this work, we show that depth estimation can be reformed from a 3D surface perspective. We propose a loss function for depth estimation that integrates the surface-aware constraints, leading to a faster and better convergence with the valid information from spatial information. In addition, camera parameters are incorporated into the training pipeline to increase the control and transparency of the depth estimation. We also integrate a specularity removal module to recover more buried image information. Quantitative experimental results on endoscopic datasets and user studies with medical professionals demonstrate the effectiveness of our method.
