\begin{teaserfigure}
	\centering
	\setlength{\resLen}{1.27in}
	\addtolength{\tabcolsep}{-3.5pt}
	\begin{tabular}{ccccc}
		\begin{overpic}[height=\resLen]{results/disco/origin.png}
			\put(2, 3){\small \bfseries \color{white} (a) Initial}
		\end{overpic}
		&
		\begin{overpic}[height=\resLen]{results/disco/deriv0_ad_better.png}
			\put(2, 3){\small \bfseries \color{white} (b) Est. derivative}
		\end{overpic}
		&
		\begin{overpic}[height=\resLen]{results/disco/rot_only/final.png}
			\put(2, 3){\small \bfseries \color{white} (c) Optimized}
		\end{overpic}
		&
		\begin{overpic}[height=\resLen]{results/disco/rot_only/target.png}
			\put(2, 3){\small \bfseries \color{white} (d) Target}
		\end{overpic}
		&
		\begin{overpic}[height=\resLen]{results/disco/rot_only/loss.pdf}
		\end{overpic}
	\end{tabular}
	\caption{\label{fig:teaser}
		We introduce a new differential theory of radiative transfer, which lays the foundation for computing the derivatives of radiometric measures with respect to arbitrary scene parameterizations (e.g., material properties and object geometries).
		The ability to evaluate these derivatives can facilitate gradient-based optimization for many diverse applications. 
		As an example, here we optimize the pose of a dodecahedron emitting colored beams inside a participating medium.
		Given a target image~(d) and an initial configuration~(a), the optimization uses derivatives estimated by our method~(b) to find parameters that produce rendered images~(c) closely matching the target.
		Per-iteration optimization loss and difference between true and estimated parameters (both measured in $\mathrm{L}_2$) are plotted on the right.
	}
\end{teaserfigure}
