  Recently, Neural Radiance Fields (NeRF) has shown promising performances on
reconstructing 3D scenes and synthesizing novel views from a sparse set of 2D
images. Albeit effective, the performance of NeRF is highly influenced by the
quality of training samples. With limited posed images from the scene, NeRF
fails to generalize well to novel views and may collapse to trivial solutions
in unobserved regions. This makes NeRF impractical under resource-constrained
scenarios. In this paper, we present a novel learning framework, ActiveNeRF,
aiming to model a 3D scene with a constrained input budget. Specifically, we
first incorporate uncertainty estimation into a NeRF model, which ensures
robustness under few observations and provides an interpretation of how NeRF
understands the scene. On this basis, we propose to supplement the existing
training set with newly captured samples based on an active learning scheme. By
evaluating the reduction of uncertainty given new inputs, we select the samples
that bring the most information gain. In this way, the quality of novel view
synthesis can be improved with minimal additional resources. Extensive
experiments validate the performance of our model on both realistic and
synthetic scenes, especially with scarcer training data. Code will be released
at \url{https://github.com/LeapLabTHU/ActiveNeRF}.
