  A number of problems in computer vision and related fields would be mitigated
if camera spectral sensitivities were known. As consumer cameras are not
designed for high-precision visual tasks, manufacturers do not disclose
spectral sensitivities. Their estimation requires a costly optical setup, which
triggered researchers to come up with numerous indirect methods that aim to
lower cost and complexity by using color targets. However, the use of color
targets gives rise to new complications that make the estimation more
difficult, and consequently, there currently exists no simple, low-cost, robust
go-to method for spectral sensitivity estimation. Furthermore, even if not
limited by hardware or cost, researchers frequently work with imagery from
multiple cameras that they do not have in their possession. To provide a
practical solution to this problem, we propose a framework for spectral
sensitivity estimation that not only does not require any hardware, but also
does not require physical access to the camera itself. Similar to other work,
we formulate an optimization problem that minimizes a two-term objective
function: a camera-specific term from a system of equations, and a universal
term that bounds the solution space. Different than other work, we use publicly
available high-quality calibration data to construct both terms. We use the
colorimetric mapping matrices provided by the Adobe DNG Converter to formulate
the camera-specific system of equations, and constrain the solutions using an
autoencoder trained on a database of ground-truth curves. On average, we
achieve reconstruction errors as low as those that can arise due to
manufacturing imperfections between two copies of the same camera. We provide
our code and predicted sensitivities for 1,000+ cameras, and discuss which
tasks can become trivial when camera responses are available.
