  Colonoscopy is the most common procedure for early detection and removal of
polyps, a critical component of colorectal cancer prevention. Insufficient
visual coverage of the colon surface during the procedure often results in
missed polyps. To mitigate this issue, reconstructing the 3D surfaces of the
colon in order to visualize the missing regions has been proposed. However,
robustly estimating the local and global coverage from such a reconstruction
has not been thoroughly investigated until now. In this work, we present a new
method to estimate the coverage from a reconstructed colon pointcloud. Our
method splits a reconstructed colon into segments and estimates the coverage of
each segment by estimating the area of the missing surfaces. We achieve a mean
absolute coverage error of 3-6\% on colon segments generated from synthetic
colonoscopy data and real colonography CT scans. In addition, we show good
qualitative results on colon segments reconstructed from real colonoscopy
videos.
