Gradient-domain rendering estimates finite difference gradients of image intensities and reconstructs the final result by solving a screened Poisson problem, which shows improvements over merely sampling pixel intensities. Adaptive sampling is another orthogonal research area that focuses on distributing samples adaptively in the primal domain. However, adaptive sampling in the gradient domain with low sampling budget has been less explored. Our idea is based on the observation that signals in the gradient domain are sparse, which provides more flexibility for adaptive sampling. We propose a deep-learning-based end-to-end sampling and reconstruction framework in gradient-domain rendering, enabling adaptive sampling gradient and the primal maps simultaneously. We conducted extensive experiments for evaluation and showed that our method produces better reconstruction quality than other methods in the test dataset.