ActiveNeRF: Learning where to See with Uncertainty Estimation

Xuran Pan, Zihang Lai, Shiji Song, Gao Huang
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引用次数: 27

Abstract

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}.
ActiveNeRF:学习在不确定性评估中看到什么
近年来,神经辐射场(Neural Radiance Fields, NeRF)在重建3D场景和从稀疏的2D图像合成新视图方面表现出了良好的性能。尽管NeRF是有效的,但其性能受到训练样本质量的高度影响。由于来自场景的有限的摆拍图像,NeRF不能很好地推广到新的视图,并且可能在未观察到的区域崩溃为平凡的解决方案。这使得NeRF在资源受限的情况下不切实际。在本文中,我们提出了一个新的学习框架,ActiveNeRF,旨在模拟一个具有有限输入预算的3D场景。具体来说,我们首先将不确定性估计纳入NeRF模型,该模型确保了在少量观察下的鲁棒性,并提供了NeRF如何理解场景的解释。在此基础上,我们提出基于主动学习方案,用新捕获的样本补充现有的训练集。通过评估给定新输入的不确定性的减少,我们选择带来最多信息增益的样本。通过这种方式,可以用最少的额外资源来提高新视图合成的质量。大量的实验验证了我们的模型在真实场景和合成场景上的性能,特别是在训练数据较少的情况下。代码将在\url{https://github.com/LeapLabTHU/ActiveNeRF}上发布。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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