EvaSurf:高效的视图意识隐含的纹理表面重建

相关实验视频

Updated: Jul 21, 2026

10:21

Evanescent Field Based Photoacoustics: Optical Property Evaluation at Surfaces

Published on: July 26, 2016

11.6K

Jingnan Gao, Zhuo Chen, Yichao Yan

IEEE transactions on visualization and computer graphics

|March 3, 2025

概括

EvaSurf 在移动设备上实现了高效的实时3D对象重建. 这种新的方法通过精确的网格和视觉感知纹理实现了高保真效果,使其适用于日常应用.

科学领域:

计算机视觉计算机视觉
3D重建的3D重建
计算机图形计算机图形

背景情况:

传统的3D重建方法难以实现实时性能和高保真输出.
像神经辐射场 (NeRF) 这样的差分染技术提供了高保真性,但在计算上昂贵.
由于染运行时间的限制,现有的方法通常对日常应用不切实际.

研究的目的:

开发一种适合移动设备的高效3D重建方法.
为了实现高保真网状重建,使用准确,视图依赖的纹理.
在常见的移动硬件上实现实时染性能.

主要方法:

介绍了EvaSurf,一个高效的View-Aware隐性纹理表面重建方法.
采用了高效的基于表面的模型,具有多视图监控,用于准确的网格生成.
学习了一种隐式纹理,具有视觉意识编码,用于高保真,视觉依赖的染.
利用轻量级的神经遮光器来降低计算成本和实时性能.

主要成果:

EvaSurf可以在合成和现实数据集上重建高质量的外观和准确的网格.
该方法在移动设备上以每秒40 (FPS) 以上的速度实现实时性能.
训练是高效的,只需要1-2小时的单个GPU,与一个小的最终包装大小 (40-50 MB).

结论:

EvaSurf提供了一个实用的解决方案,用于移动设备上的实时3D重建.
该方法成功地平衡了高保真外观和精确的几何与计算效率.
这种方法显著提高了3D重建在日常移动场景中的适用性.

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