Gao Changbo, Sun Qiucheng, Zhu Jinlong, Chen Jie
Changchun Normal University, Changchun, 130032, China.
Sci Rep. 2025 Feb 12;15(1):5275. doi: 10.1038/s41598-025-88614-z.
The recent advance in Neural Radiance Fields (NeRF), which utilizes Multilayer Perceptrons (MLP) for implicit scene representation, enables the synthesis of realistic views from new perspectives. However, degradation in the quantity and quality of input images can lead to failure in scene reconstruction and difficulty in synthesizing high-quality views. To address these limitations, this paper presents a NeRF-based framework (MBS-NeRF), which can reconstruct sharp NeRF from a limited number of motion-blurred input images for high-quality view synthesis. The framework integrates depth information as a constraint to counter the lack of sufficient view information and introduces a Motion Blur Simulation Module (MBSM) to simulate the physical formation process of motion blur. We further introduce camera trajectory optimization during the exposure process to robust the incorrect camera position. MBS-NeRF is thoroughly trained considering photometric consistency and depth supervision. Comprehensive experiments on synthetic and real datasets validate the effectiveness of the model in reconstructing sharp NeRF and achieving high-quality view synthesis from sparse, motion-blurred inputs.
神经辐射场（NeRF）的最新进展利用多层感知器（MLP）进行隐式场景表示，能够从新视角合成逼真的视图。然而，输入图像数量和质量的下降会导致场景重建失败以及难以合成高质量视图。为了解决这些限制，本文提出了一种基于NeRF的框架（MBS-NeRF），它可以从有限数量的运动模糊输入图像中重建清晰的NeRF，以进行高质量视图合成。该框架将深度信息作为一种约束来应对视图信息不足的问题，并引入了一个运动模糊模拟模块（MBSM）来模拟运动模糊的物理形成过程。我们还在曝光过程中引入相机轨迹优化，以纠正不正确的相机位置。MBS-NeRF在考虑光度一致性和深度监督的情况下进行了充分训练。在合成数据集和真实数据集上进行的综合实验验证了该模型在重建清晰NeRF以及从稀疏、运动模糊的输入中实现高质量视图合成方面的有效性。
