Abstract
Analyzing sources and causes of error in multi-view scene reconstruction is difficult. In the absence of any ground-truth information, reprojection error is the only valid metric to assess error. Unfortunately, inspecting reprojection error values does not allow computer vision researchers to attribute a cause to the error. A visualization technique to analyze errors in sequential multi-view reconstruction is presented. By computing feature track summaries, researchers can easily observe the progression of feature tracks through a set of frames over time. These summaries easily isolate poor feature tracks and allow the observer to infer the cause of a delinquent track. This visualization technique allows computer vision researchers to analyze errors in ways previously unachieved. It allows for a visual performance analysis and comparison between feature trackers, a previously unachieved result in the computer vision literature. This framework also provides the foundation to a number of novel error detection and correction algorithms.