the lowest errors are the “posters" and “grace" sequence. The latter half of the “underground" sequence, as well as the image pair in the “there" sequence, exhibits medium errors whereas the “color" sequence exhibits large errors. In the “color" sequence, the best performing method is “hpdct-25". Nevertheless, the error values indicate that all methods in discussion do not provide satisfactory results on this sequence.

Error Prediction Experiment
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In this experiment, we evaluate the usage of a Linear Predictor to predict its expected error as described in Sec. \[subsec:error\_prediction\]. We collect a dataset of 5 planar targets shown in Fig. \[fig:error\_prediction\](b), with each planar target consisting of 11 view points encapsulating the target. This allows us to compute an average test error for each keypoint which can then be compared against the predicted average error. After extracting a bounding box surrounding the planar target in each image, a homography is computed between the first and every other image in the data set. This homography is then refined using dense image alignment on all the pixels in the bounding box. These steps are illustrated in Fig. \[fig:error\_prediction\](a).

We estimate the ground-truth affine warps in a manner similar to Sec. \[subsec:real\_experiment\]. To 