ATM-NeRF:通过几何正则化加速训练移动设备上的NeRF渲染

IF 9.7 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yang Chen;Lin Zhang;Shengjie Zhao;Yicong Zhou
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引用次数: 0

摘要

近年来,越来越多的研究人员致力于将神经辐射场(NeRF)令人印象深刻的新颖视图合成能力转移到资源受限的移动设备上。一个常见的解决方案是预训练NeRF并将其烘烤成纹理网格,这是由移动图形硬件很好地支持的。然而,即使使用多个高端NVIDIA V100 gpu,现有方法的训练过程也往往需要几个小时。其根本原因是这些方案主要依赖于光度渲染损失,而忽略了预训练NeRF与烘烤结果之间的几何关系。基于这一点,我们提出了ATM-NeRF(基于NeRF的移动渲染加速训练),它是第一个在预训练和烘烤训练阶段应用有效的几何正则化约束以更快收敛的算法。具体而言,在初始NeRF预训练阶段,我们加强了代表场景几何的多分辨率密度网格的一致性,以在一定程度上缓解形状-亮度模糊问题,实现了平滑的粗网格。在第二阶段,我们利用预训练的深度投影的3D点的位置和几何特征为粗糙网格的几何和外观的联合精细提供几何监督。因此,我们的ATM-NeRF实现了与MobileNeRF相当的渲染质量,其训练速度约为30倍,同时保持了导出网格的更精细的结构细节。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ATM-NeRF: Accelerating Training for NeRF Rendering on Mobile Devices via Geometric Regularization
Recently, an increasing number of researchers have been dedicated to transferring the impressive novel view synthesis capability of Neural Radiance Fields (NeRF) to resource-constrained mobile devices. One common solution is to pre-train NeRF and bake it into textured meshes which are well supported by mobile graphics hardware. However, the training process of existing methods often requires several hours even with multiple high-end NVIDIA V100 GPUs. The underlying reason is that these schemes mainly rely on photometric rendering loss, neglecting the geometric relationship between the pre-trained NeRF and the baked results. Standing on this point, we present ATM-NeRF (Accelerating Training for Mobile rendering based on NeRF), which is the first to apply effective geometric regularization constraints during both the pre-training and the baking training stages for faster convergence. Specifically, in the initial NeRF pre-training stage, we enforce consistency of the multi-resolution density grids representing the scene geometry to mitigate the shape-radiance ambiguity problem to some extent, achieving a coarse mesh with smoothness. In the second stage, we utilize the positions and geometric features of 3D points projected from the pre-trained posed depths to provide geometric supervision for joint refinement of geometry and appearance of the coarse mesh. As a result, our ATM-NeRF achieves comparable rendering quality to MobileNeRF with a training speed that is about $30\times \sim 70\times$ faster while maintaining finer structure details of the exported mesh.
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来源期刊
IEEE Transactions on Multimedia
IEEE Transactions on Multimedia 工程技术-电信学
CiteScore
11.70
自引率
11.00%
发文量
576
审稿时长
5.5 months
期刊介绍: The IEEE Transactions on Multimedia delves into diverse aspects of multimedia technology and applications, covering circuits, networking, signal processing, systems, software, and systems integration. The scope aligns with the Fields of Interest of the sponsors, ensuring a comprehensive exploration of research in multimedia.
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