Parameterization of Volume Sampling for Active Learning of Radiance Field

Radiance field techniques have demonstrated remarkable performance in reconstructing photorealistic real-world scenes. However, a widely recognized limitation of these methods is their reliance on densely captured images for training. Active learning offers a promising solution by selecting the most informative images to capture. An effective measure of data informativeness is the mutual information between the unknown image and the model parameters, known as the expected information gain. Naive estimation of this quantity requires inferring both the model and predictive distributions, which is computationally intractable for high-dimensional parameter spaces. In this work, we propose a computationally tractable method to estimate information gain. Instead of sampling in the high-dimensional model parameter space, we leverage the ray sampling process inherent in volume rendering to approximate the expected information gain. Specifically, we parameterize volume sampling with perturbed ray directions and learn the predictive distribution to infer optimal perturbation patterns. Furthermore, we derive an empirical risk decomposition that demonstrates how our method effectively explores the volume sampling space to enhance diversity, leading to efficient and informative view selection. Experiments show that our approach achieves state-of-the-art performance in image quality for active learning of radiance fields, outperforming previous methods across various datasets, including both forward-facing and object-centric scenes.