Towards disparity joint upsampling for robust stereoscopic endoscopic scene reconstruction in robotic prostatectomy

Abstract

Three-dimensional (3-D) scene reconstruction from stereoscopic binocular laparoscopic videos is an effective way to expand the limited surgical field and augment the structure visualization of the organ being operated in minimally invasive surgery. However, currently available reconstruction approaches are limited by image noise, occlusions, textureless and blurred structures. In particular, an endoscope inside the body only has the limited light source resulting in illumination non-uniformities in the visualized field. These limitations unavoidably deteriorate the stereo image quality and hence lead to low-resolution and inaccurate disparity maps, resulting in blurred edge structures in 3-D scene reconstruction. This paper proposes an improved stereo correspondence framework that integrates cost-volume filtering with joint upsampling for robust disparity estimation. Joint bilateral upsampling, joint geodesic upsampling, and tree filtering upsampling were compared to enhance the disparity accuracy. The experimental results demonstrate that joint upsampling provides an effective way to boost the disparity estimation and hence to improve the surgical endoscopic scene 3-D reconstruction. Moreover, the bilateral upsampling generally outperforms the other two upsampling methods in disparity estimation.