1. Push the boundary handling logic into the underlying array
object. This has two very significant impacts:
a. The interpolation code becomes extremely simple to write
and to test.
b. The user has more flexibility in implementing how out of bounds
values are handled. We provide one default implementation.
Change-Id: Ic2f6cf9257ce7110c62e492688e5a6c8be1e7df2
Since Ceres is moving to using GitHub for issues, and the Google
Code URL in the current copyright header will soon become invalid,
update all the headers.
Change-Id: I1fce70375d1bcf098591f07b4d8f01a5c1e0789c
The key change is that there is a new layer of abstract,
a Array object that the interpolator depends on.
The Array provides a one dimension or two dimensional
array like interface independent of the underlying representation
of the data.
Also included here is support for vector valued functions.
Change-Id: Ica68f03778cf0d84192db00cd55653f8b4124d51
This bi-cubic interpolation implementation is based
on the cubic convolution algorithm of keys, which allows
us to implement a bi-cubic spline like interpolation scheme
using five one dimensional cubic spline operations.
Change-Id: I116aa8036191c3e654af788323fc8298ae8252a6
Add a cubic interpolator based on the Catmull-Rom spline,
with support for automatic differentiation.
Change-Id: I02ae4c4ea37805ff1f717b05ea805989b474bd59
Sameer Agarwal