  In computer vision, reference datasets have been highly successful in
promoting algorithmic development in stereo reconstruction. Surgical scenes
gives rise to specific problems, including the lack of clear corner features,
highly specular surfaces and the presence of blood and smoke. Publicly
available datasets have been produced using CT and either phantom images or
biological tissue samples covering a relatively small region of the endoscope
field-of-view. We present a stereo-endoscopic reconstruction validation dataset
based on CT (SERV-CT). Two {\it ex vivo} small porcine full torso cadavers were
placed within the view of the endoscope with both the endoscope and target
anatomy visible in the CT scan. Orientation of the endoscope was manually
aligned to the stereoscopic view. Reference disparities and occlusions were
calculated for 8 stereo pairs from each sample. For the second sample an RGB
surface was acquired to aid alignment of smooth, featureless surfaces. Repeated
manual alignments showed an RMS disparity accuracy of ~2 pixels and a depth
accuracy of ~2mm. The reference dataset includes endoscope image pairs with
corresponding calibration, disparities, depths and occlusions covering the
majority of the endoscopic image and a range of tissue types. Smooth specular
surfaces and images with significant variation of depth are included. We
assessed the performance of various stereo algorithms from online available
repositories. There is a significant variation between algorithms, highlighting
some of the challenges of surgical endoscopic images. The SERV-CT dataset
provides an easy to use stereoscopic validation for surgical applications with
smooth reference disparities and depths with coverage over the majority of the
endoscopic images. This complements existing resources well and we hope will
aid the development of surgical endoscopic anatomical reconstruction
algorithms.
